improve abstract

.gitattributes

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.gitignore

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 topic.pdf
 thesis.pdf
 proposal.pdf
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+ l 478.633 60.095 l h
+f
+Q Q
+showpage
+%%Trailer
+count op_count sub {pop} repeat
+countdictstack dict_count sub {end} repeat
+cairo_eps_state restore
+%%EOF

25_Outline/outline.tex

@@ -0,0 +1,185 @@
+%% thesis.tex
+%% 
+%% Karlsruhe Institute of Technology
+%% Institute for Program Structures and Data Organization
+%% Chair for Software Design and Quality (SDQ)
+%%
+%% Dr.-Ing. Erik Burger
+%% burger@kit.edu
+%%
+%% See https://sdqweb.ipd.kit.edu/wiki/Dokumentvorlagen
+%%
+%% {$HeadURL: https://svnserver.informatik.kit.edu/i43/svn/lehre/Abschlussarbeiten-Vorlage/thesis.tex $}
+%% {$LastChangedDate: 2018-08-02 14:19:06 +0200 (Do, 02 Aug 2018) $}
+%% {$LastChangedRevision: 5705 $}
+%% {$LastChangedBy: burger $}
+
+%% Available page modes: oneside, twoside
+%% Available modes: draft, final (see README)
+\documentclass[oneside, english, final]{sdqthesis}
+
+
+%% ---------------------------------
+%% | Information about the thesis  |
+%% ---------------------------------
+
+%% Name of the author
+\author{Hannes F. Kuchelmeister}
+
+%% Title (and possibly subtitle) of the thesis
+\title{Decision Support for Group-Based Configuration using Recommender Systems}
+
+%% Type of the thesis 
+\thesistype{Bachelor's Thesis Outline}
+
+%% Change the institute here, ``IPD'' is default
+% \myinstitute{Institute for \dots}
+
+%% You can put a logo in the ``logos'' directory and include it here
+%% instead of the SDQ logo
+% \grouplogo{myfile}
+%\secondgrouplogo{iism_logo}
+%\companylogo{cas_software_logo}
+%\externallogo{fzi_logo}
+
+%% Alternatively, you can disable the group logo
+\nogrouplogo
+\nosecondgrouplogo
+\nocompanylogo
+\noexternallogo
+
+%% The reviewers are the professors that grade your thesis
+\reviewerone{Prof. Dr. Ralf H. Reussner}
+\reviewertwo{Prof. Dr. Christof Weinhardt}
+
+%% The advisors are PhDs or Postdocs
+\advisorone{Dr. rer. nat. Robert Heinrich}
+%% The second advisor can be omitted
+\advisortwo{M.A. Jonas Fegert}
+
+%% Please enter the start end end time of your thesis
+\usepackage{texdate}
+\initdate{2019}{12}{16}
+\editingtime{\printfdate{british}}{\advancebymonths{4}\printfdate{british}}
+
+\settitle
+
+%% --------------------------------
+%% | Settings for word separation |
+%% --------------------------------
+
+%% Describe separation hints here.
+%% For more details, see 
+%% http://en.wikibooks.org/wiki/LaTeX/Text_Formatting#Hyphenation
+\hyphenation{
+% me-ta-mo-del
+}
+
+%% --------------------
+%% |  Float Barriers  |
+%% --------------------
+\usepackage{placeins}
+
+%% --------------------
+%% |  Table Packages  |
+%% --------------------
+\usepackage{tabularx}
+\usepackage{multirow}
+
+%% --------------------
+%% |  Maths Packages  |
+%% --------------------
+\usepackage{amsmath}
+\usepackage{amssymb}
+
+%% --------------------
+%% | Plotting Packages |
+%% --------------------
+\usepackage{pgfplots}
+
+%% ---------------------
+%% | Generating Frames |
+%% ---------------------
+\usepackage[framemethod=TikZ]{mdframed}
+
+%% --------------------------------
+%% | PDF Comments                 |
+%% --------------------------------
+\usepackage[colorinlistoftodos, obeyDraft]{todonotes}
+
+%% --------------------------------
+%% | Gantt Charts                 |
+%% --------------------------------
+\usepackage{pgfgantt}
+
+%% --------------------------------
+%% | Quotation                 |
+%% --------------------------------
+\usepackage{csquotes}
+\MakeOuterQuote{"}
+
+%% --------------------------------
+%% | Bibliography                 |
+%% --------------------------------
+
+%% Use biber instead of BibTeX, see README
+\usepackage[citestyle=numeric,style=numeric,sorting=none,backend=biber]{biblatex}
+\addbibresource{outline.bib}
+
+%% ====================================
+%% ====================================
+%% ||                                ||
+%% || Beginning of the main document ||
+%% ||                                ||
+%% ====================================
+%% ====================================
+\begin{document}
+
+%% Set PDF metadata
+\setpdf
+
+%% Set the title
+\maketitle
+
+%% The Preamble begins here
+\frontmatter
+
+\setcounter{page}{1}
+\pagenumbering{roman}
+
+%% ------------------------
+%% |   Table of Contents  |
+%% ------------------------
+\tableofcontents
+
+\listoffigures
+\listoftables
+
+%% -----------------
+%% |   Main part   |
+%% -----------------
+
+\mainmatter
+
+\input{sections/00_introduction.tex}
+\input{sections/20_foundations.tex}
+\input{sections/30_related_work.tex}
+\input{sections/40_concept.tex}
+\input{sections/50_design_and_implementation.tex}
+\input{sections/60_evaluation.tex}
+\input{sections/70_further_work.tex}
+
+%% --------------------
+%% |   Bibliography   |
+%% --------------------
+
+%% Add entry to the table of contents for the bibliography
+\printbibliography[heading=bibintoc]
+
+%% ----------------
+%% |   Appendix   |
+%% ----------------
+\appendix
+%\input{sections/appendix.tex}
+
+\end{document}

25_Outline/sdqthesis.cls

@@ -0,0 +1,273 @@
+%% LaTeX2e class for student theses
+%% sdthesis.cls
+%%
+%% Karlsruhe Institute of Technology
+%% Institute for Program Structures and Data Organization
+%% Chair for Software Design and Quality (SDQ)
+%%
+%% Dr.-Ing. Erik Burger
+%% burger@kit.edu
+%%
+%% Version 1.3.3, 2018-04-17
+%%
+%% SVN-Informationen:
+%% ------------------
+%% $LastChangedBy: burger $
+%% $LastChangedRevision: 5524 $
+%% $LastChangedDate: 2018-04-17 16:49:27 +0200 (Di, 17 Apr 2018) $
+%% $HeadURL: https://svnserver.informatik.kit.edu/i43/svn/lehre/Abschlussarbeiten-Vorlage/sdqthesis.cls $
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesClass{sdqthesis}[2017/08/01 v1.3.2 Student thesis class]
+% Language options. German is also needed in English thesis for the abstract
+\DeclareOption{ngerman}{\PassOptionsToPackage{\CurrentOption}{babel}}
+\DeclareOption{english}{\PassOptionsToPackage{main=\CurrentOption,ngerman}{babel}}
+% Oneside and twoside layout
+\DeclareOption{oneside}{\PassOptionsToClass{\CurrentOption,BCOR=10mm}{scrbook}}
+\DeclareOption{twoside}{\PassOptionsToClass{\CurrentOption,BCOR=20mm}{scrbook}}
+\ProcessOptions\relax
+% Used to detect language families
+\RequirePackage{translations}
+
+%% ---------------------------------------------------------------
+%% | Based on KOMAscript by Markus Kohm http://www.komascript.de |
+%% ---------------------------------------------------------------
+\LoadClass[12pt,a4paper,titlepage=true,DIV=15,headsepline]{scrbook}
+
+%% ---------------
+%% | Page Layout |
+%% ---------------
+\RequirePackage[headsepline,draft=false]{scrlayer-scrpage}
+\pagestyle{scrheadings}
+
+% For one-sided layout, put the page marks on the right margin
+\if@twoside%
+\else%
+\chead[]{}
+\ohead{\headmark}
+\cfoot[]{}
+\ofoot[\pagemark]{\pagemark}
+\fi%
+
+\clubpenalty=10000 %prevent orphans
+\widowpenalty=10000 %prevent widows
+
+%% --------------
+%% | Typography |
+%% --------------
+
+% T1 font encoding
+\RequirePackage[T1]{fontenc}
+\RequirePackage[utf8]{inputenc}
+
+% serif type: Linux Libertine
+\RequirePackage{libertine}
+% Linux Libertine in math mode
+\RequirePackage[libertine]{newtxmath}
+
+% grotesque type: Adobe Source Sans Pro
+\RequirePackage[scaled=.92]{sourcesanspro}
+
+% monospace type: Bera Mono
+\RequirePackage[scaled=.78]{beramono}
+
+% semi-bold type (for subsections and paragraphs)
+\newcommand*{\sbdefault}{sb}
+\DeclareRobustCommand{\sbseries}{%
+  \not@math@alphabet\sbseries\relax
+  \fontseries\sbdefault\selectfont}
+
+\DeclareTextFontCommand{\textsb}{\sbseries}
+
+\addtokomafont{subsection}{\sbseries}
+\addtokomafont{subsubsection}{\sbseries}
+\addtokomafont{paragraph}{\sbseries}
+
+% microtype for nicer typography
+\RequirePackage[protrusion=true,expansion=true]{microtype}
+
+% commands for code and model keywords in text
+\newcommand{\code}[1]{\texttt{\hyphenchar\font45\relax #1}}
+\newcommand{\model}[1]{\textsf{#1}}
+
+% nicer spacing for enumerations
+\RequirePackage{enumitem}
+
+%% --------------
+%% | Title page |
+%% --------------
+
+% Logos
+\makeatletter
+
+\newcommand{\theexternallogo}{sdqlogo}
+\newcommand{\externallogo}[1]{\renewcommand{\theexternallogo}{#1}}
+\newcommand{\printexternallogo}{\includegraphics[height=1.8cm]{logos/\theexternallogo}}
+\newcommand{\noexternallogo}{\renewcommand{\printexternallogo}{}}
+
+\newcommand{\thecompanylogo}{sdqlogo}
+\newcommand{\companylogo}[1]{\renewcommand{\thecompanylogo}{#1}}
+\newcommand{\printcompanylogo}{\includegraphics[height=1.8cm]{logos/\thecompanylogo}}
+\newcommand{\nocompanylogo}{\renewcommand{\printcompanylogo}{}}
+
+\newcommand{\thesecondgrouplogo}{sdqlogo}
+\newcommand{\secondgrouplogo}[1]{\renewcommand{\thesecondgrouplogo}{#1}}
+\newcommand{\printsecondgrouplogo}{\includegraphics[height=1.8cm]{logos/\thesecondgrouplogo}}
+\newcommand{\nosecondgrouplogo}{\renewcommand{\printsecondgrouplogo}{}}
+
+\newcommand{\thegrouplogo}{sdqlogo}
+\newcommand{\grouplogo}[1]{\renewcommand{\thegrouplogo}{#1}}
+\newcommand{\printgrouplogo}{\includegraphics[height=1.8cm]{logos/\thegrouplogo}}
+\newcommand{\nogrouplogo}{\renewcommand{\printgrouplogo}{}}
+
+\titlehead{%
+% KIT logo german/english
+\ifcurrentbaselanguage{English}
+{\includegraphics[height=1.8cm]{logos/kitlogo_en_cmyk}}
+{\includegraphics[height=1.8cm]{logos/kitlogo_de_cmyk}}
+\hfill
+% SDQ logo
+\printexternallogo
+\hspace{5mm}
+\printcompanylogo
+\hspace{5mm}
+\printsecondgrouplogo
+\hspace{5mm}
+\printgrouplogo
+\hspace{5mm}
+\ThisCenterWallPaper{1}{title-background.pdf}
+}
+
+%
+\setkomafont{title}{\huge\sffamily\bfseries}
+\setkomafont{subtitle}{\normalfont\large}
+
+\uppertitleback{Karlsruher Institut für Technologie\\ Fakultät für Informatik\\ Postfach 6980\\ 76128 Karlsruhe}
+
+%% variables for title page
+\newcommand{\theinstitute}{\ifcurrentbaselanguage{English}
+    {Institute for Program Structures and Data Organization (IPD)}
+    {Institut für Programmstrukturen und Datenorganisation (IPD)}}
+\newcommand{\thethesistype}{}
+\newcommand{\thereviewerone}{}
+\newcommand{\thereviewertwo}{}
+\newcommand{\theadvisorone}{}
+\newcommand{\theadvisortwo}{}
+\newcommand{\theeditstart}{}
+\newcommand{\theeditend}{}
+
+%% formatting commands for titlepage
+\newcommand{\thesistype}[1]{\subtitle{\vskip2em #1 \ifcurrentbaselanguage{English}{of}{von}}%
+\renewcommand{\thethesistype}{#1}}
+\newcommand{\myinstitute}[1]{\renewcommand{\theinstitute}{#1}}
+\newcommand{\reviewerone}[1]{\renewcommand{\thereviewerone}{#1}}
+\newcommand{\reviewertwo}[1]{\renewcommand{\thereviewertwo}{#1}}
+\newcommand{\advisorone}[1]{\renewcommand{\theadvisorone}{#1}}
+\newcommand{\advisortwo}[1]{\renewcommand{\theadvisortwo}{#1}}
+
+\newcommand{\editingtime}[2]{%
+\renewcommand{\theeditstart}{#1}%
+\renewcommand{\theeditend}{#2}%
+%% do not show the date
+\date{}
+}
+
+\newcommand{\settitle}{%
+\publishers{%
+\large
+\ifcurrentbaselanguage{English}{at the Department of Informatics}%
+{an der Fakultät für Informatik}\\
+\theinstitute\\[2em]
+\begin{tabular}{l l}
+  \ifcurrentbaselanguage{English}{Reviewer}{Erstgutachter}: & \thereviewerone\\
+  \ifcurrentbaselanguage{English}{Second reviewer}{Zweitgutachter}: & \thereviewertwo\\
+  \ifcurrentbaselanguage{English}{Advisor}{Betreuender Mitarbeiter}: &  \theadvisorone\\
+  % if there is no second advisor, do not output this line
+  \ifthenelse{\equal{\theadvisortwo}{}}{}{%
+  \ifcurrentbaselanguage{English}{Second advisor}{Zweiter betreuender Mitarbeiter}: & \theadvisortwo\\
+  }
+\end{tabular}
+\vskip2em
+\theeditstart{} -- \theeditend
+}
+}
+
+%% -----------------------------
+%% | Abstract/Acknowledgements |
+%% -----------------------------
+
+\newcommand{\abstract}[1][\abstractname]{\chapter*{#1}}
+\newcommand{\Abstract}[1][\abstractname]{\chapter*{#1}\addcontentsline{toc}{chapter}{#1}}
+
+\def\ackname{Acknowledgments}
+\def\abstractname{Abstract}
+\def\switcht@deutsch{\svlanginfo
+	\def\ackname{Danksagung}
+	\def\abstractname{Kurzfassung}
+}
+\def\switcht@english{\svlanginfo
+	\def\ackname{Acknowledgements}
+	\def\abstractname{Abstract}
+}
+
+%% In English theses, an additional German Abstract is needed.
+\newcommand{\includeabstract}{
+\ifcurrentbaselanguage{English}{
+% include English and German abstracts
+\input{sections/abstract_en.tex}
+\begin{otherlanguage}{ngerman}
+\input{sections/abstract_de.tex}
+\end{otherlanguage}
+}{
+% include only German abstract
+\include{sections/abstract_de}
+}
+}
+
+%% ------------
+%% | Packages |
+%% ------------
+
+% draft mode
+\RequirePackage{ifdraft}
+\RequirePackage{ifthen}
+
+% enumerate subsubsections
+\setcounter{secnumdepth}{3}
+
+% wallpaper for title page (KIT-Frame)
+\RequirePackage{wallpaper}
+
+% subimport of files
+\RequirePackage{import}
+
+% languages
+\RequirePackage{babel}
+
+% hyphenation for compound words
+\RequirePackage[shortcuts]{extdash}
+
+% nice tables
+\RequirePackage{booktabs}
+\RequirePackage{longtable}
+\RequirePackage{array}
+
+% show graphics in draft mode
+\RequirePackage{graphicx}
+\setkeys{Gin}{draft=false}
+
+% appendix
+\RequirePackage[toc,title,header]{appendix}
+\noappendicestocpagenum
+
+% PDF specific packages
+\RequirePackage[hyphens]{url}
+\RequirePackage[breaklinks,colorlinks=false]{hyperref}
+\newcommand\setpdf{\hypersetup{%
+    pdftitle={\thethesistype},%
+    pdfsubject={\@title},%
+    pdfauthor={\@author},%
+    pdfborder={0 0 0},%
+    }%
+    \let\theauthor\@author
+}

25_Outline/sections/00_introduction.tex

@@ -0,0 +1,46 @@
+\chapter{Introduction}
+\label{ch:Introduction}
+
+\section{Problem}
+\label{sec:Introduction:Problem}
+
+A group of people with different personal preferences wants to find a solution to a problem with high variability. Making decisions in the group comes with problems as a lack of communication leads to worse decision outcomes \cite{atasItemRecommendationUsing2017}. Group dynamics and biases can lead to suboptimal decisions \cite{kerrBiasJudgmentComparing1996}. Generally group decisions are complex and often the process that yields the decision result is unstructured, thereby not providing any reproducibility of the success. Groups have different power structures and usually individuals have different interests. Moreover finding solutions is a rather complex task and group decisions can suffer intransparency.
+
+Examples of group recommendation decisions are:
+\begin{itemize}
+    \item A companies truck fleet (e.g. driver, purchasing-manager, marketing manager)
+    \item A companies customer management software system (e.g. salesperson, human resource manager, accounting manager)
+    \item A public project to get a new area at a zoo (e.g. visitor, director, animal keeper)
+    \item Managing a forest (e.g. owner, environmental protection agency, consumer representative)
+    \item An existing company building and how it should be furnished (e.g. landlord, employee representative, CEO)
+\end{itemize}
+
+These examples are different from an ordinary group decision in the sense that the components the solution is build from are standardised but a solution is highly individual because of high variability. The solution space therefore is rather large.
+
+\section{Idea}
+\label{sec:Introduction:Idea}
+
+To support groups in their decision making product configuration can be used. It allows to accurately map constraints and dependencies in complex problems and to map the solution space. Using a group recommender a group is supported in their configuration decisions. The goal is to show that these approaches can help a group with the configuration task presented by the usage of a configurator and to better process individual preferences than a human can.
+
+
+There does not exists much research on recommendation for group configuration, however it is comprised of two different areas of research, recommenders for groups and recommenders for configuration.
+The existing literature on recommenders for groups is extensive with many different approaches and domains \cite{delicResearchMethodsGroup2016, chenInterfaceInteractionDesign2011, atasItemRecommendationUsing2017, jamesonRecommendationGroups2007, chenEmpatheticonsDesigningEmotion2014, liuCGSPAComprehensiveGroup2019}. \citeauthor{felfernigGroupRecommenderSystems2018} give an overview about basic approaches \cite{felfernigGroupRecommenderSystems2018}.
+In the area of product configuration research about recommender systems is undertaken as well \cite{pereiraFeatureBasedPersonalizedRecommender2016, scholzConfigurationbasedRecommenderSystem2017, scholzEffectsDecisionSpace2017}.
+Group configuration is a more specialized sub field of configuration therefore less attention has been directed towards it and because of that there has not been much research on recommenders that are for group recommendation in a configuration setting.
+
+\section{Benefits}
+\label{sec:Introduction:Benefits}
+
+The benefits of this approach are, that the need for a group to communicate directly is reduced. Each user gives their own preferences and the group will get a recommendation based on that. This allows to reduce problems arising in groups decisions like lack of communication and bias in groups. Additionally this shows the viability of combining group recommendations and configuration approaches.
+
+\section{Action}
+\label{sec:Introduction:Action}
+
+As resulting action in this thesis a prototype will be designed, implemented and evaluated that achieves the following objectives.
+\begin{itemize}
+    \item A system should give recommendation for the group using a scoring function that takes into account preferences of group members and the current state of the situation.
+    \item Recommendations should allow different scoring functions.
+    \item Recommendations should always be valid options.
+    \item The system should consider all preferences of individuals in a group and find a solution that most people of the group are happy with.
+\end{itemize}
+The results are communicated through this thesis. Thereby adhering to the research method of design science research \cite{peffersDesignScienceResearch2007}.

25_Outline/sections/20_foundations.tex

@@ -0,0 +1,223 @@
+\chapter{Foundations}
+\label{ch:Foundations}
+
+\section{Product Configuration}
+\label{sec:Foundations:ProductConfiguration}
+
+Product configuration is a process consisting of a series of decision tasks whereby a product is constructed of components which interact with each other. During a configuration process no new components are created. Their interplay and specification is defined beforehand \cite[~ pp. 42, 43]{sabinProductConfigurationFrameworksa1998}. This section defines product configuration analogous to \citeauthor{felfernigOpenConfiguration2014} \cite{felfernigOpenConfiguration2014}.
+
+Formally a configuration problem can be specified as a \emph{constraint satisfaction problem (CSP)} \cite{tsangFoundationsConstraintSatisfaction1993} as 
+\begin{equation} \label{eq:Foundations:ProductConfiguration:ConstraintSatisfactionProblem}
+    CSP(V,\mathfrak{D},C),
+\end{equation}
+where we have a set of \emph{variables} $V$ (which in this thesis will also be referred to as \emph{features}) with
+\begin{equation} \label{eq:Foundations:ProductConfiguration:Variables}
+    V = \{v_1, \dotsc, v_m\},
+\end{equation}
+a \emph{domain mapping} $\mathfrak{D}$ that maps variable to their corresponding domain values $D$ (which in this thesis will be also referred to as \emph{characteristics}) i.e. the values that can be assigned to each variable
+\begin{equation}\label{eq:Foundations:ProductConfiguration:DomainMapping}
+    \mathfrak{D} : V \to D; x \mapsto \mathfrak{D}(x) \qquad where \ D = \{d_1, \dotsc, d_o\},
+\end{equation}
+and \emph{constraints} $C$ that limit the solution space with 
+\begin{equation}\label{eq:Foundations:ProductConfiguration:Constraints}
+    C = \{c_1, \cdots, c_k\}.
+\end{equation}
+
+\subsection{Configuration State}
+
+We will define a \emph{configuration} $S$ as a tuple of variables (\autoref{eq:Foundations:ProductConfiguration:Variables}) and their corresponding domain value with
+\begin{equation} \label{eq:Foundations:ProductConfiguration:ConfigurationState}
+    S = \{ (v_i,\ d) \ |\ v_i \in V \ \land \ d \in \mathfrak{D}(i),\ i=1,\dotsc,m \}.
+\end{equation}
+Essentially it is a set of variables and assigned values.
+
+\subsection{Finished Configuration}
+To define a \emph{finished configuration} we first need to define what a valid configuration is. Therefore we define $is\_valid$ as
+\begin{equation} \label{eq:Foundations:ProductConfiguration:IsValid}
+    is\_valid : S \to \{true, false\}; x \mapsto 
+    \begin{cases}
+        true, & S \in solution\_space \\
+        false, & \text{otherwise}
+    \end{cases},
+\end{equation}
+with $solution\_space$ being the solution space of the corresponding constraint satisfaction problem. A \emph{finished configuration} $S_F$ is a configuration that contains all variables and is a valid configuration:
+\begin{equation} \label{eq:Foundations:ProductConfiguration:FinishedConfiguration}
+    S_F \subset S,\ where \ \forall v_i \in V (\exists (v_i, d) \in S_F : d \in \mathfrak{D}(i)) \land is\_valid(S_F).
+\end{equation}
+In practice a finished configuration of a product (or solution) is something that is ready to be produced. For example if we are configuring a car, this means that the car could be produced in the specified way that is given by the finished configuration.
+
+
+\section{Group-Based Product Configuration}
+\label{sec:Foundations:GroupBasedProductConfiguration}
+
+Instead of a single person configuring a product, a group of people is configuring one product which can be useful in multi-stakeholder decisions. This setting needs mechanisms for describing the preferences of multiple people. Therefore we will add to our definitions, a set of users $U$ with
+\begin{equation}\label{eq:Foundations:ProductConfiguration:Users}
+    U = \{1, \dotsc, n\},
+\end{equation}
+a users \emph{utility function} that maps a domain value to a utility value and is only known to the user
+\begin{equation}
+    \begin{split}
+        u_i(d_j), \qquad \text{where}\ & d_j \in  \mathfrak{D}(j),\\
+        & 1 <= j <= m, \\
+        & 1 <= i <= n .
+    \end{split}
+\end{equation}
+\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}'s approach, which this thesis extends, is to treat the group configuration the same as one shared configuration and to sync the selection of attributes across clients \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}. 
+
+\section{Recommender System}
+\label{sec:Foundations:RecommenderSystem}
+
+A recommender system is a system that gives individualized recommendations to users to guide them through a large space of objects \cite[~ p. 331]{burkeHybridRecommenderSystems2002}.
+
+There are several approaches to recommender systems presented in \cite{felfernigGroupRecommenderSystems2018}, these are: collaborative filtering, constraint-based recommendation, content-based filtering and hybrid recommendation.
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & The Matrix & Titanic & Die Hard & Forest Gump & Wall-E \\ \hline
+         John  & 5 & 1 &   & 2 & 2 \\
+         Lucy  & 1 & 5 & 2 & 5 & 5 \\
+         Eric  & 2 & ? & 3 & 5 & 4 \\
+         Diane & 4 & 3 & 5 & 3 &   \\
+    \end{tabular}
+    \caption{An example showing users ratings for movies by \citeauthor{ningComprehensiveSurveyNeighborhoodBased2015} \cite{ningComprehensiveSurveyNeighborhoodBased2015}.}
+    
+    \label{tab:Foundations:RecommenderSystem:MoviePreferences}
+\end{table}
+
+\subsection{Collaborative Filtering}
+In collaborative filtering a users rating for unknown items is predicted by finding similar users who have rated it. Their rating is used as prediction
+\cite[~ pp. 7, 8]{felfernigDecisionTasksBasic2018}.
+
+Collaborative Filtering can not only be done using users, it can also be item-based. Hereby the similarity between items is used for a recommendation and not similar users \cite{ricciRecommenderSystemsHandbook2015}. In the context of configuration the similarity to other historic configurations can be used which makes it an item based approach. 
+
+\autoref{tab:Foundations:RecommenderSystem:MoviePreferences} shows an example rating matrix. A simple user-based way to calculate a rating would be to use a k-nearest neighbour (kNN) algorithm and then take the average of those ratings. Using this method with $k := 2$ and euclidean distance our closest neighbours are \textit{Lucy} and \textit{Diane} therefore giving us a predicted rating of $4$.  If we use an item based approach instead, we will try to find similar items based on the users rating. An example of similar items here would be \textit{Forest Gump} and \textit{Wall-E} as John and Lucy each have given them the sane rating and Eric's rating is off by one. Using again kNN with $k := 2$ we find that \textit{Forest Gump} and \textit{Wall-E} are the most similar to \textit{Titanic} thereby having a predicted rating of $4.5$.
+However this simple similarity and prediction function does not take into account different distances. For example Lucy's ratings are more similar compared to Eric's than Diane's but Diane's and Lucy's rating is valued the same amount.
+
+\subsection{Constraint-Based Recommendation}
+Hereby filter rules are defined which filter out items that don't fulfil specified rules. A user models their requirements with these rules and thereby gets a list of recommended items. This approach requires deep knowledge about a product because it needs a detailed description of features  \cite[~ p. 12]{felfernigDecisionTasksBasic2018}.
+
+Our movie example (see \autoref{tab:Foundations:RecommenderSystem:MoviePreferences}) requires additional information for example about plot structure, pacing, length and other attributes of the movie. Now the user could enter filtering criteria, e.g. that the movie should be no longer than 120 minutes, be categorized as action or thriller and have a fast pacing. The system will only recommend movies that fit into these categories.
+
+\subsection{Content-Based Filtering}
+For the content-based filtering approach, items and users are assigned to categories. Based on consumption and rating of items a user will have implicit ratings for categories. Predictions are now made based on a categories of the new item \cite[~ pp. 10, 11]{felfernigDecisionTasksBasic2018}.
+
+Using our example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} and using an additional category matrix (see \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}) we can derive a rating matrix per category (using the average rating of the user of each movie contained in this category). The result can be seen in \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}. To predict Eric's rating of Titanic we now can use the categories of \textit{Titanic} and average out Eric's implicit rating per category. Titanic is only in the category romance and as Eric's rating of \textit{Forest Gump} is $5$ the prediction is a rating of $5$. Categories don't have to be the genre, they could be any kind of data about a movie.
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & The Matrix & Titanic & Die Hard & Forest Gump & Wall-E \\ \hline
+         Action  & x &  & x  &  &  \\
+         Sci-Fi  & x &  &  &  &  \\
+         Thriller  &  & & x &  &  \\
+         Romance & & x & & x & \\
+         Family & & & & x & x \\
+    \end{tabular}
+    \caption{Showing example categories for movies in \autoref{tab:Foundations:RecommenderSystem:MoviePreferences}.}
+    
+    \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}
+\end{table}
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & Action & Sci-Fi & Thriller & Romance & Family \\ \hline
+        John  & 5 & 5 & & 1.5 & 2 \\
+        Lucy  & 1.5 & 1 & 2 & 5 & 5 \\
+        Eric  & 2.5 & 2 & 3 & 5 & 4.5 \\
+        Diane & 4.5 & 4 & 5 & 3 & 3  \\
+    \end{tabular}
+    \caption{User profiles generated from categories and rating from \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} and \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}.}
+    
+    \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}
+\end{table}
+
+\subsubsection{Advantages over Collaborative Filtering}
+\begin{itemize}
+    \item No cold start problem for items
+    \item No grey sheep problem as not dependent on similar groups having existed before.
+    \item Domain knowledge is existent
+    \item No issues with data sparsity as item description is given by product structure
+    \item No reliance on preferences that would result in a comparison space that is too large
+    \item No dependence of historic group preference accuracy 
+\end{itemize}
+
+\subsubsection{Advantages over Constrained-Based Recommendation}
+
+\begin{itemize}
+    \item Configuration state does not cause absence of recommendations
+    \item Expendable to also support constraints 
+    \item No need to handle inconsistencies explicitly
+\end{itemize}
+
+
+\begin{table}
+    \begin{center}
+        \begin{tabularx}{\columnwidth}{X|X|X}
+            & advantages & disadvantages \\
+            \hline
+            Collaborative Filtering 
+            &   \begin{itemize}
+                    \item Serendipity of results 
+                    \item Automatic learning of market segments
+                    \item No domain knowledge required
+                \end{itemize}
+            &   \begin{itemize}
+                    \item Cold start problem for users and items
+                    \item Grey sheep problem
+                    \item Quality based on rating quality
+                    \item Data sparsity
+                    \item Privacy not guaranteed
+                \end{itemize} \\
+            \hline
+            Content-Based Filtering 
+            &   \begin{itemize}
+                    \item No community required 
+                    \item User independent
+                    \item Transparent
+                    \item No item cold start
+                    \item Simplicity
+                    \item Robust
+                    \item Stable to constant influx of new users
+                \end{itemize}
+            &   \begin{itemize}
+                    \item Overspecialisation
+                    \item No serendipity
+                    \item User cold start problem
+                    \item Requires domain knowledge
+                \end{itemize} \\
+            \hline
+            Constraint-Based Recommendation 
+            &   \begin{itemize}
+                    \item Transparent
+                    \item Good for non discrete values
+                \end{itemize}
+            &   \begin{itemize}
+                    \item Inconsistent constraints
+                    \item No results
+                \end{itemize} \\ 
+        \end{tabularx}
+        \caption{A description of the advantages and disadvantages of common recommendation techniques \cite{richthammerSituationAwarenessRecommender2018, shokeenStudyFeaturesSocial2019,hahslerRecommenderlabFrameworkDeveloping2015, aminiDiscoveringImpactKnowledge2011, suSurveyCollaborativeFiltering2009}}
+        \label{tab:Foundations:RecommenderComparison}
+    \end{center}
+\end{table}
+
+\subsection{Hybrid Recommendation}
+A hybrid recommender combines different recommendation approaches to use the strengths of each individual one and to reduce effects of weaknesses \cite{burkeHybridRecommenderSystems2002}.
+
+\section{Group Recommender System}
+\label{sec:Foundations:GroupRecommenderSystem}
+
+A group recommender system is a recommender system aimed at making recommendations for a group instead of a single user. To make recommendations group members preferences have to be aggregated. This can be done by either aggregating single user recommendations or by merging preferences of each user into a group preference model. Based on the resulting preference model recommendation strategies as described in \autoref{sec:Foundations:RecommenderSystem} can be used to generate recommendations \cite{jamesonRecommendationGroups2007}.
+
+For a group recommender system additional definitions are needed. The attitude of a user is represented by their preferences $P$ which is directly related to the utility a user has from a domain value being present in the configuration. Let 
+\begin{gather} \label{tab:Foundations:GroupRecommenderSystem:Preferences}
+    P = \{ P_1, \dotsc, P_n\},\ \text{where} \\
+    P_i = \{(d,\ u_i(d)) \ | \ \forall d \in \mathfrak{D}(i),\ i=1,\dotsc,m \} \notag
+\end{gather}
+
+
+\FloatBarrier
+
+

25_Outline/sections/30_related_work.tex

@@ -0,0 +1,37 @@
+\chapter{Related Work}
+\label{ch:Related_Work}
+
+\section{Group Recommender Systems}
+\label{sec:Related_Work:GroupRecommender}
+
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{choudharyMulticriteriaGroupRecommender2020}] propose a multi-criteria group recommender system \cite{choudharyMulticriteriaGroupRecommender2020}. An analytical hierarchy process is used to learn priorities for film features like story, action, direction and to then make a number of best recommendations for a group of users. Their approach works well for film selection and they observe that it is easier to make recommendations for homogenous groups than for random groups. Also small groups receive better recommendations compared to large ones.
+
+    \item[\citeauthor{chenInterfaceInteractionDesign2011}] looks at interface and interaction designs that supports the overall group and does not only consider each user individually. Chen designs a music recommendation system \emph{GroupFun} with a focus on groups that tries to enhance mutual awareness and transparency. \citeauthor{chenInterfaceInteractionDesign2011}'s assessment is that this work is still in a preliminary stage\cite{chenInterfaceInteractionDesign2011}. Further work in that area was conducted by \citeauthor{chenEmpatheticonsDesigningEmotion2014} looking at emotional awareness in groups and how that can be visualised in a user interface \cite{chenEmpatheticonsDesigningEmotion2014}. 
+\end{description}
+
+\section{Group-Based Configuration}
+\label{sec:Related_Work:GroupBasedConfiguration}
+
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}] builds a collaborative configurator based on the CAS Merlin Configurator. Here groups of people are able to simultaneously configure a product \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}. If there are any conflicts, a conflict resolution process is started. However the prototype only allows a majority voting approach and does not provide any further group decision support. Also this process only starts upon the selection of an invalid state that gives alternatives.
+
+    \item[\citeauthor{felferningGroupBasedConfiguration2016}] introduce basic definitions of group based configuration task, show what conflicts can occur, how to deal with inconsistencies in preferences among group members and how to integrate different decision heuristics into this process \cite{felferningGroupBasedConfiguration2016}.
+
+    \item[\citeauthor{velasquez-guevaraMultiSPLOTSupportingMultiuser2018}] implement a web based simultaneous group-based configuration system that uses constraint programming \cite{velasquez-guevaraMultiSPLOTSupportingMultiuser2018}. Hereby each user configures according to their own preferences and the system proposes a configuration according to different strategies. These strategies are maximization of selections (which means finding the solution with the largest number of features selected), minimizing conflicting selections (which means to minimize the inclusion of features that have conflicts) and to prioritize decisions from some users.
+\end{description}
+
+\section{Recommender Systems for Configuration}
+\label{sec:Related_Work:RecommenderSystemsForConfiguration}
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{rubinshteynEntwicklungHybridenRecommender2018}] looks at different approaches to recommendation and implements a prototype with CAS Merlin Configurator which uses a hybrid recommender system \cite{rubinshteynEntwicklungHybridenRecommender2018}. His prototype combines constraint-based filtering with collaborative filtering and achieves better results in terms of precision than non-hybrid systems using collaborative filtering and constraint-based recommendation. In terms of recall his hybrid does not surpass the high numbers of constraint-based recommendation but improves upon collaborative-filtering. 
+
+    \item [\citeauthor{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}] uses a constraint based recommender that uses fuzzy logic to relax constraints and thereby reducing the amount of times where the recommender is unable to make recommendations \cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}. With his approach a product manager has direct influence on the recommendations. Rules for recommendations hereby are not automatically learned but only manually created and relate to predefined user interest categories.
+
+    \item [\citeauthor{ullmannEntwurfUndUmsetzung2017}] implements a recommendation engine that is able to estimate customer budgets, a k-nearest neighbour classifier for finding a base configuration and non-negative matrix factorization combined with nearest neighbour to find configurations for specific users \cite{ullmannEntwurfUndUmsetzung2017}. \par
+
+    \item[\citeauthor{wetzelPersonalisierterUndLernender2017}] combines collaborative filtering and click-stream analysis \cite{wetzelPersonalisierterUndLernender2017}. For collaborative filtering he implements three filtering algorithms: k-nearest neighbour, weighted majority voting and non-negative matrix factorization. Collaborative filtering is used to find configurations that are similar to the current configuration. Click-stream analysis is done by using n-grams and the Smith-Waterman algorithm. \citeauthor{wetzelPersonalisierterUndLernender2017} also tries to use click-stream data in combination with Markov chains to give recommendations on how configuration options should be ordered in a configuration form but click-streams do not yield any improvements in terms of accuracy and recall.
+
+    \item[\citeauthor{falknerRecommendationTechnologiesConfigurable2011}] provide an overview of recommendation approaches for configuration to improve usability of configuration systems \cite{falknerRecommendationTechnologiesConfigurable2011}. They look at feature recommender to recommend which features in a configuration would be useful to have and at value recommender for these features. Additionally they discuss approaches for ranking and recommending explanations for inconsistencies between customers requirements and product rules however they do not provide any recommendations and point towards further needed research.
+\end{description}
+

25_Outline/sections/40_concept.tex

@@ -0,0 +1,169 @@
+\chapter{Concept}
+\label{ch:Concept}
+
+\section{User Interaction with the System}
+\label{sec:Concept:UserSystemInteraction}
+
+The system has one main way to be used as defined in \autoref{tab:Concept:MainUseCase}. This process is also visualized in \autoref{fig:Concept:ConfigurationProcess}.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/bpmn_configuration_process_with_continious_recommendation.pdf}
+    \caption{A BPMN diagram of the configuration process.}
+    \label{fig:Concept:ConfigurationProcess}
+\end{figure}
+
+\begin{table}
+    \begin{center}
+        \begin{tabularx}{\columnwidth}{l|X}
+            \multicolumn{2}{c}{Main System Usage} \\
+            \hline
+            Preconditions   & 
+                \begin{itemize}
+                    \item The configurator is opened with the same session on each of the group member's machines
+                    \item The configuration is in an unfinished state (this state is a consensus state)
+                \end{itemize} \\
+            \hline
+            Postcondition   & 
+                \begin{itemize}
+                    \item All users have entered their preferences for each attribute explicitly.
+                    \item The system gives a recommendation based on all preferences and the unfinished configuration state
+                \end{itemize} \\
+            \hline
+            Basic Flow      & 
+                \begin{enumerate}
+                    \item A user indicates a preference for an attribute
+                    \item The system generates a recommendation (based on preferences and configuration status)
+                    \item If not all users have given their preferences go to step 1.
+                \end{enumerate} \\
+            \hline
+        \end{tabularx}
+        \caption{A description of the main way users will interact with the system}
+        \label{tab:Concept:MainUseCase}
+    \end{center}
+\end{table}
+
+\section{Case Study}
+\label{sec:Concept:CaseStudy}
+
+The case study used in this thesis is a simplified version from forestry.
+The used characteristics and attributes are shown in \autoref{fig:Concept:ForestExample}. Additionally as example are given preferences, a configuration state and a finished configuration.
+
+\begin{figure}
+    \begin{mdframed}[frametitle={Example for Forest Use Case}]
+        In this example we have a small group of users. The use case is a piece of forest and variables are for example harvesting activity, which trees to grow and accessibility for people.
+        \begin{align}
+            \begin{split}
+                V = \{ & \textit{Heimisch}, \textit{Klimaresilient}, \textit{Verwertbar}, \textit{Ernteaufwand}, \\
+                & \textit{Menge}, \textit{Preis}, \textit{Walderfahrung} \},
+            \end{split} \notag \\
+            \mathfrak{D}(\textit{Heimisch}) =  \{ & \text{Gering}, \text{Mittel}, \text{Hoch}\}, \notag \\
+            \mathfrak{D}(\textit{Klimaresilient}) = \{ & \text{Gering}, \text{Mittel}, \text{Hoch}\}, \notag \\
+            \mathfrak{D}(\textit{Verwertbar}) = \{ & \text{Gering}, \text{Mittel}, \text{Hoch}\}, \notag \\
+            \mathfrak{D}(\textit{Ernteaufwand}) = \{ & \text{Motormanuel}, \text{Harvester}, \text{Vollautomatisch}\}, \notag \\
+            \mathfrak{D}(\textit{Menge}) = \{ & \text{Keine}, \text{Gering}, \text{Hoch}\}, \notag \\
+            \mathfrak{D}(\textit{Preis}) = \{ & \text{Gering}, \text{Mittel}, \text{Hoch}\}, \notag\\
+            \mathfrak{D}(\textit{Walderfahrung}) = \{ & \text{Gering}, \text{Mittel}, \text{Intensiv}\},\notag \\
+            U = \{ & 1,2\} \notag\\
+            P = \{ & P_1, P_2\} \notag\\
+            \begin{split}
+                P_1 = \{ & (\text{Motormanuel}, 0.5), (\text{Harvester}, -0.3) \} \\ 
+                & \cup \{ (d,0)\ |\ d \in \mathfrak{D}(i),\ i \in V,\ i \notin \{ \text{Motormanuel}, \text{Harvester}\} \ \} \ 
+            \end{split} \notag \\
+            P_2 = \{ & (d,0)\ |\ d \in \mathfrak{D}(i),\ i \in V \} \notag \\
+            S  =  \{ & (\textit{Heimisch}, \text{Gering}), (\textit{Menge}, \text{Gering}) \} \notag \\
+            \begin{split}
+            S_F  =  \{ & (\textit{Heimisch}, \text{Gering}), (\textit{Klimaresilient}, \text{Gering}), (\textit{Verwertbar}, \text{Gering}), \\
+            & (\textit{Ernteaufwand}, \text{Motormanuel}),
+            (\textit{Menge}, \text{Keine}), (\textit{Preis}, \text{Hoch}),\\ 
+            & (\textit{Walderfahrung}, \text{Gering}) \} 
+            \end{split} \notag
+        \end{align}
+    \end{mdframed}
+    \caption{An example of a forest use case that includes two people.}
+    \label{fig:Concept:ForestExample}
+    \end{figure}
+
+
+\section{Solution Generation}
+\label{sec:Concept:SolutionGeneration}
+
+Given an unfinished configuration and preferences of all group members, rate a finished configuration on how well it reflects the configuration state and preferences. This Use this to choose the best finished configuration out of a list to recommend.
+
+\subsection{Generating a Recommendation}
+
+Hereby the idea is there is a database of complete configurations (possibly historic from other groups or automatically generated or both).
+Now the recommendation procedure looks as follows:
+
+\begin{enumerate}
+    \item Assign a score to each stored configuration according to $$score_{group}(\overline{configurationState},\ \overline{preferences}, \ configurationInStore)$$
+    \item Optional: Filter out configurations that have a score below a certain value using a different scoring function. For example filter out configurations that cause a certain level of misery.
+    \item Chose the configuration with the highest score as recommendation.
+\end{enumerate}
+
+\begin{samepage}
+\subsection{Scoring Function}
+\label{subsec:Concept:SolutionGeneration:ScoringFunction}
+\emph{Group configuration scoring function} includes preferences and current configuration state. This function gives a score for a finished configuration (while using the current configuration state and all user preferences):
+\begin{equation}
+    score_{group}: S \times P \times S_F \to \mathbb{R}
+\end{equation}
+
+An example group configuration scoring function is $score_{group}$ with
+
+\begin{equation}
+    \notag \alpha \in \mathbb{R}, \qquad     changed(d,\overline{s}, s) = 
+    \begin{cases}
+      1, & d \in \overline{s} \land d \notin s \\
+      0, & \text{otherwise}
+    \end{cases}
+\end{equation}
+
+\begin{equation}
+    \begin{split}
+        score_{group}(\overline{s},\ \overline{p},\ s)
+        & = score(\overline{p},\ s) - penalty(\overline{s},\ s) \\
+        & = score(\overline{p},\ s) - \sum_{d \in \overline{s}} changed(d,\overline{s}, s) \cdot \alpha
+    \end{split}
+\end{equation}
+
+This thesis will use multiple scoring functions. Among those are ones for least misery, average and multiplicative. Average and multiplicative yield good results among the studies presented by \citeauthor{Masthoff2015}. Strategies can also be combined, one example here is average without misery \cite{Masthoff2015}.
+\end{samepage}
+
+\subsubsection{Preference Scoring}
+
+All of the aggregation functions mentioned in \autoref{subsec:Concept:SolutionGeneration:ScoringFunction} use one preference per user per product. Therefore to use them in as is a score for the whole configuration per user has to be calculated. We propose to use the difference from the selected feature compared to the average rating of all characteristics. This approach includes all preferences of a user meaning a preference is also seen relative to others.
+
+As an example we could have feature
+\begin{equation}
+    F = \text{ClimateResilientTrees},
+\end{equation} with characteristics
+\begin{equation}
+    \mathfrak{D}(F)= \{\text{low}, \text{medium}, \text{high}\},
+\end{equation}
+preferences
+\begin{equation}
+    P_1 = \{(\text{low}, 0), (\text{medium},0.6), (\text{high},0.9) \}
+\end{equation} 
+and the configuration we want to rate
+\begin{equation}
+    S_F = \{(\text{ClimateResilientTrees}, \text{high})\}.
+\end{equation}
+The average rating for the feature is $F = 0.5$. Therefore the score given by a user $1$ is $0.9-0.5 = 0.4$.
+A second user with preferences 
+\begin{equation}
+    P_2 = \{(\text{low}, 0), (\text{medium},0), (\text{high},0.9) \}
+\end{equation} 
+on the other hand results in a feature score of $0.9-0.3=0.6$. For this user characteristic \emph{high} is of higher importance.
+
+As we would like to keep our scores as percentages and not in the interval $[-1,1]$ a normalisation is applied by adding one and dividing by two. Therefore our respective scores are $0.7$ for user one and $0.95$ for user two. A configuration usually consists of more than one feature therefore we take the average rating over all features to get the score one user gives to a configuration. Based on that score the in \autoref{subsec:Concept:SolutionGeneration:ScoringFunction} mentioned aggregation functions can be used.
+
+\subsubsection{Cofiguration Change Penalty}
+
+In this thesis a penalty function is proposed which gives the percentage of characteristics that exist in the configuration that is to be rated. This value can be tuned to be more or less strict by potentiating. Thereby allowing more deviation or less deviation from the current configuration state. The penalty function is defined as
+\begin{equation}
+    penalty_{proportion}(\overline{s},\ s) =  \left(\frac{\sum_{d \in \overline{s}} changed(d,\overline{s}, s)}{|\overline{s}|}\right)^\alpha.
+\end{equation}
+
+By including the current configuration into scoring the scoring function can take into account, changes that have been already implemented and therefore might be very costly to change.
+    

25_Outline/sections/50_design_and_implementation.tex

@@ -0,0 +1,55 @@
+\chapter{Design and Implementation}
+\label{ch:DesignImplementation}
+
+
+\section{CAS Configurator Merlin}
+\label{sec:DesignImplementation:ConfiguratorMerlin}
+
+\autoref{fig:DesignImplementation:ConfiguratorMerlin} shows the architecture of CAS Configurator Merlin.
+\begin{description}
+    \item[M.Core] provides the base of the configurator. It checks the configuration against all rules in the database, provides possible alternatives if a change invalidates other parts of a configuration. The system relies on a CSP solver for validation and suggestion of alternatives.
+    \item[M.Model] is the editor to create products and rules. These rules can then be uploaded to M.Core.
+    \item[M.Customer] is the customer facing component. It allows a customer to configure a product.
+\end{description}
+
+\begin{figure}
+    \centering
+    \includegraphics{./figures/MerlinConfigurator.pdf}
+    \caption{Architecture of Configurator Merlin \cite[Fig. 4.1]{raabKollaborativeProduktkonfigurationEchtzeit2019}}
+    \label{fig:DesignImplementation:ConfiguratorMerlin}
+\end{figure}
+
+\section{CAS Group-Configurator}
+\label{sec:DesignImplementation:GroupConfigurator}
+
+\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}'s extends CAS Merlin Configurator in his thesis to allow simultaneous configuration. The extended architecture is shown in \autoref{fig:DesignImplementation:CollaborativeConfiguratorMerlin}.
+He only makes changes to M.Customer which is renamed to M.Collab-Customer and introduces a new component M.Collab.
+
+\begin{description}
+    \item[M.Collab] is a node.js server application that communicates with M.Core via REST-API and with M.Collab-Customer via WebSocket. It sits in between M.Collab-Customer and M.Core and handles all processing regarding collaborative configuration.
+    \item[M.Collab-Customer] a modified version of M.Customer that does all communication via WebSocket and does communicate with M.Collab instead of M.Core.
+\end{description}
+
+\begin{figure}
+    \centering
+    \includegraphics{./figures/MerlinCollaborativeConfigurator.pdf}
+    \caption{Architecture of Collaborative Configurator Merlin \cite[Fig. 4.3]{raabKollaborativeProduktkonfigurationEchtzeit2019}}
+    \label{fig:DesignImplementation:CollaborativeConfiguratorMerlin}
+\end{figure}
+
+
+\section{Extended Configurator}
+\label{sec:DesignImplementation:ExtendedConfigurator}
+
+Extending \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} work a module called M.Recommender is added. Its aim is to provide a recommendation server that holds all the data needed for recommendations. M.Collab and M.Collab-Customer have to be modified to allow taking in preferences and to show  recommendations. The recommender engine is set up to be in a separate system which allows the easier replacement and the usage of different technologies. The extended architecture is shown in \autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}.
+
+\begin{description}
+    \item[M.Recommender] is a new system that will get queried from M.Collab for recommendations, when the configuration changes. M.Recommender will return recommendations which then can be presented to users by M.Collab-Customer.
+\end{description}
+
+\begin{figure}
+    \centering
+    \includegraphics{./figures/MerlinCollabRecommender.pdf}
+    \caption{Architecture of Collaborative Configurator Merlin with an added recommender system.}
+    \label{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}
+\end{figure}

25_Outline/sections/60_evaluation.tex

@@ -0,0 +1,89 @@
+\chapter{Evaluation}
+\label{ch:Evaluation}
+
+In this chapter the prototype is evaluated in terms of its functionality and its properties.
+
+We will generate all possible valid configurations for one use case i.e. generate all possible valid configurations for the forest use case.
+
+Generate groups with preferences (explicit preferences) and configuration state (which would be for example the currently existing forest).
+
+\section{Group Types During Evaluation}
+\label{sec:Evaluation:GroupTypes}
+
+\begin{itemize}
+    \item Groups shall be generated with random preferences
+    \item With grouped preferences: people adhere more or less to one profile (Forest Owner, Athlete, Consumer, Environmentalist)
+    \item Group of only one profile type: rather homogenous group
+\end{itemize}
+
+\section{Metrics}
+For the evaluation metrics to evaluate by are needed. 
+
+\label{sec:Evaluation:Metrics}
+
+\subsection{Random Individual}
+When comparing a group to individual scores, a member of the group is randomly chosen. As metric of difference used metrics are MSE, RMSE and similar metrics.
+% see: https://medium.com/@george.drakos62/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-1-regrression-metrics-3606e25beae0 or https://en.wikipedia.org/wiki/Error_metric
+
+\subsection{Satisfaction}
+As a metric on overall satisfaction within the group we propose a threshold metric that defines a user as satisfied if his score is above a threshold of 60\% and as unsatisfied with a score of less than 40\%. Now we can measure group satisfaction by amount of members being satisfied, neutral and unsatisfied.
+
+\subsection{Group Score}
+The group score metric is to simply take the score the recommender has given to a group. This score can be compared with other configurations' score.
+
+
+\section{Questions to Answer During the Evaluation}
+\label{sec:Evaluation:Questions}
+
+\begin{itemize}
+    \item Main question: How does the group decision differ from the decision of a single decision maker? (random individual)
+    \item How much impact does the configuration state have on the recommendation outcome? (random individual, satisfaction)
+    \item How many group members are satisfied by the group decision on average? (satisfaction)
+    \item Is the recommender fair, i.e. no user type is always worse off than others? (satisfaction)
+    \item How does the amount of stored finished configurations relate to recommendation quality? (Compare the recommended configuration to the best possible configuration in terms of score using MSE and similar and in terms of satisfaction and random individual) 
+    \item How much higher is the score of the best configuration compared to average? (group score, satisfaction, random individual)
+\end{itemize}
+
+\section{Generating Data}
+\label{sec:Evaluation:GeneratingGroups}
+
+For the forest use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude to an attribute value. Now during data generation the attitude is converted to a preference using a normal distribution. \autoref{fig:Evaluation:DataGeneration} shows how we convert the user profile to preferences.
+
+\pgfplotsset{height=5cm,width=\textwidth,compat=1.8}
+\pgfmathdeclarefunction{gauss}{2}{%
+  \pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}%
+}
+\begin{figure}
+    \begin{tikzpicture}
+        \begin{axis}[
+            every axis plot post/.append style={
+                mark=none, domain=0:1, samples=50, smooth
+            },
+            axis x line*=bottom,
+            xmin=0,
+            xmax=1,
+            ymin=0.1,
+            xticklabel style={
+                /pgf/number format/precision=3,
+            },
+            xtick={0,0.25, 0.5, 0.75,1},
+            hide y axis]
+          \addplot [draw=red][very thick] {gauss(0.25,0.1)} node[text=red][above,pos=0.5] {negative};
+          \addplot [draw=blue][very thick] {gauss(0.5,0.05)} node[text=blue][above,pos=0.48] {neutral};
+          \addplot [draw=green!60!black][very thick] {gauss(0.75,0.1)} node[text=green!60!black][above,pos=0.5] {positive};
+        \end{axis}
+        \end{tikzpicture}
+ \caption{Distribution of preferences for a user type.}
+\label{fig:Evaluation:DataGeneration}
+\end{figure}
+
+These user profiles can be used to generate rather homogenous groups but also to create groups that have interests that are more conflicting. For completely random groups a uniform distribution is used to create more chaotic groups. The whole process is shown in \autoref{fig:Evaluation:GeneratingDataProcess}.
+
+
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/bpmn_evaluation_input_data_generation.pdf}
+    \caption{The process used for generating data for the evaluation.}
+    \label{fig:Evaluation:GeneratingDataProcess}
+\end{figure}

25_Outline/sections/70_further_work.tex

@@ -0,0 +1,11 @@
+\chapter{Further Work}
+\label{ch:FurtherWork}
+
+\section{Possible Extensions or Further Research}
+\label{sec:FurtherWork:PossibleExtensions}
+
+\begin{itemize}
+    \item How to optimise such that no need to search through all stored finished configurations is necessary? (e.g. improve runtime from $\mathcal{O}(n)$ to $\mathcal{O}(log\ n)$)
+    \item How to model hierarchy and knowledge about product components in preferences?
+    \item Letting users set preferences for product functions (e.g. for a forest a recreation function, a productive function, a protective function, etc.). How does it compare to explicitly choosing preferences?
+\end{itemize}

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+ l 478.633 60.095 l h
+f
+Q Q
+showpage
+%%Trailer
+count op_count sub {pop} repeat
+countdictstack dict_count sub {end} repeat
+cairo_eps_state restore
+%%EOF

30_Thesis/sdqthesis.cls

@@ -0,0 +1,249 @@
+%% LaTeX2e class for student theses
+%% sdthesis.cls
+%%
+%% Karlsruhe Institute of Technology
+%% Institute for Program Structures and Data Organization
+%% Chair for Software Design and Quality (SDQ)
+%%
+%% Dr.-Ing. Erik Burger
+%% burger@kit.edu
+%%
+%% Version 1.3.3, 2018-04-17
+%%
+%% SVN-Informationen:
+%% ------------------
+%% $LastChangedBy: burger $
+%% $LastChangedRevision: 5524 $
+%% $LastChangedDate: 2018-04-17 16:49:27 +0200 (Di, 17 Apr 2018) $
+%% $HeadURL: https://svnserver.informatik.kit.edu/i43/svn/lehre/Abschlussarbeiten-Vorlage/sdqthesis.cls $
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesClass{sdqthesis}[2017/08/01 v1.3.2 Student thesis class]
+% Language options. German is also needed in English thesis for the abstract
+\DeclareOption{ngerman}{\PassOptionsToPackage{\CurrentOption}{babel}}
+\DeclareOption{english}{\PassOptionsToPackage{main=\CurrentOption,ngerman}{babel}}
+% Oneside and twoside layout
+\DeclareOption{oneside}{\PassOptionsToClass{\CurrentOption,BCOR=10mm}{scrbook}}
+\DeclareOption{twoside}{\PassOptionsToClass{\CurrentOption,BCOR=20mm}{scrbook}}
+\ProcessOptions\relax
+% Used to detect language families
+\RequirePackage{translations}
+
+%% ---------------------------------------------------------------
+%% | Based on KOMAscript by Markus Kohm http://www.komascript.de |
+%% ---------------------------------------------------------------
+\LoadClass[12pt,a4paper,titlepage=true,DIV=15,headsepline]{scrbook}
+
+%% ---------------
+%% | Page Layout |
+%% ---------------
+\RequirePackage[headsepline,draft=false]{scrlayer-scrpage}
+\pagestyle{scrheadings}
+
+% For one-sided layout, put the page marks on the right margin
+\if@twoside%
+\else%
+\chead[]{}
+\ohead{\headmark}
+\cfoot[]{}
+\ofoot[\pagemark]{\pagemark}
+\fi%
+
+\clubpenalty=10000 %prevent orphans
+\widowpenalty=10000 %prevent widows
+
+%% --------------
+%% | Typography |
+%% --------------
+
+% T1 font encoding
+\RequirePackage[T1]{fontenc}
+\RequirePackage[utf8]{inputenc}
+
+% serif type: Linux Libertine
+\RequirePackage{libertine}
+% Linux Libertine in math mode
+\RequirePackage[libertine]{newtxmath}
+
+% grotesque type: Adobe Source Sans Pro
+\RequirePackage[scaled=.92]{sourcesanspro}
+
+% monospace type: Bera Mono
+\RequirePackage[scaled=.78]{beramono}
+
+% semi-bold type (for subsections and paragraphs)
+\newcommand*{\sbdefault}{sb}
+\DeclareRobustCommand{\sbseries}{%
+  \not@math@alphabet\sbseries\relax
+  \fontseries\sbdefault\selectfont}
+
+\DeclareTextFontCommand{\textsb}{\sbseries}
+
+\addtokomafont{subsection}{\sbseries}
+\addtokomafont{subsubsection}{\sbseries}
+\addtokomafont{paragraph}{\sbseries}
+
+% microtype for nicer typography
+\RequirePackage[protrusion=true,expansion=true]{microtype}
+
+% commands for code and model keywords in text
+\newcommand{\code}[1]{\texttt{\hyphenchar\font45\relax #1}}
+\newcommand{\model}[1]{\textsf{#1}}
+
+% nicer spacing for enumerations
+\RequirePackage{enumitem}
+
+%% --------------
+%% | Title page |
+%% --------------
+
+% Logos
+\newcommand{\thegrouplogo}{sdqlogo}
+\newcommand{\grouplogo}[1]{\renewcommand{\thegrouplogo}{#1}}
+\newcommand{\printgrouplogo}{\includegraphics[height=1.8cm]{logos/\thegrouplogo}}
+\newcommand{\nogrouplogo}{\renewcommand{\printgrouplogo}{}}
+
+\titlehead{%
+% KIT logo german/english
+\ifcurrentbaselanguage{English}
+{\includegraphics[height=1.8cm]{logos/kitlogo_en_cmyk}}
+{\includegraphics[height=1.8cm]{logos/kitlogo_de_cmyk}}
+\hfill
+% SDQ logo
+\printgrouplogo
+\ThisCenterWallPaper{1}{title-background.pdf}
+}
+
+%
+\setkomafont{title}{\huge\sffamily\bfseries}
+\setkomafont{subtitle}{\normalfont\large}
+
+\uppertitleback{Karlsruher Institut für Technologie\\ Fakultät für Informatik\\ Postfach 6980\\ 76128 Karlsruhe}
+
+%% variables for title page
+\newcommand{\theinstitute}{\ifcurrentbaselanguage{English}
+    {Institute for Program Structures and Data Organization (IPD)}
+    {Institut für Programmstrukturen und Datenorganisation (IPD)}}
+\newcommand{\thethesistype}{}
+\newcommand{\thereviewerone}{}
+\newcommand{\thereviewertwo}{}
+\newcommand{\theadvisorone}{}
+\newcommand{\theadvisortwo}{}
+\newcommand{\theeditstart}{}
+\newcommand{\theeditend}{}
+
+%% formatting commands for titlepage
+\newcommand{\thesistype}[1]{\subtitle{\vskip2em #1 \ifcurrentbaselanguage{English}{of}{von}}%
+\renewcommand{\thethesistype}{#1}}
+\newcommand{\myinstitute}[1]{\renewcommand{\theinstitute}{#1}}
+\newcommand{\reviewerone}[1]{\renewcommand{\thereviewerone}{#1}}
+\newcommand{\reviewertwo}[1]{\renewcommand{\thereviewertwo}{#1}}
+\newcommand{\advisorone}[1]{\renewcommand{\theadvisorone}{#1}}
+\newcommand{\advisortwo}[1]{\renewcommand{\theadvisortwo}{#1}}
+
+\newcommand{\editingtime}[2]{%
+\renewcommand{\theeditstart}{#1}%
+\renewcommand{\theeditend}{#2}%
+%% do not show the date
+\date{}
+}
+
+\newcommand{\settitle}{%
+\publishers{%
+\large
+\ifcurrentbaselanguage{English}{at the Department of Informatics}%
+{an der Fakultät für Informatik}\\
+\theinstitute\\[2em]
+\begin{tabular}{l l}
+  \ifcurrentbaselanguage{English}{Reviewer}{Erstgutachter}: & \thereviewerone\\
+  \ifcurrentbaselanguage{English}{Second reviewer}{Zweitgutachter}: & \thereviewertwo\\
+  \ifcurrentbaselanguage{English}{Advisor}{Betreuender Mitarbeiter}: &  \theadvisorone\\
+  % if there is no second advisor, do not output this line
+  \ifthenelse{\equal{\theadvisortwo}{}}{}{%
+  \ifcurrentbaselanguage{English}{Second advisor}{Zweiter betreuender Mitarbeiter}: & \theadvisortwo\\
+  }
+\end{tabular}
+\vskip2em
+\theeditstart{} -- \theeditend
+}
+}
+
+%% -----------------------------
+%% | Abstract/Acknowledgements |
+%% -----------------------------
+
+\newcommand{\abstract}[1][\abstractname]{\chapter*{#1}}
+\newcommand{\Abstract}[1][\abstractname]{\chapter*{#1}\addcontentsline{toc}{chapter}{#1}}
+
+\def\ackname{Acknowledgments}
+\def\abstractname{Abstract}
+\def\switcht@deutsch{\svlanginfo
+	\def\ackname{Danksagung}
+	\def\abstractname{Kurzfassung}
+}
+\def\switcht@english{\svlanginfo
+	\def\ackname{Acknowledgements}
+	\def\abstractname{Abstract}
+}
+
+%% In English theses, an additional German Abstract is needed.
+\newcommand{\includeabstract}{
+\ifcurrentbaselanguage{English}{
+% include English and German abstracts
+\input{sections/abstract_en.tex}
+\begin{otherlanguage}{ngerman}
+\input{sections/abstract_de.tex}
+\end{otherlanguage}
+}{
+% include only German abstract
+\include{sections/abstract_de}
+}
+}
+
+%% ------------
+%% | Packages |
+%% ------------
+
+% draft mode
+\RequirePackage{ifdraft}
+\RequirePackage{ifthen}
+
+% enumerate subsubsections
+\setcounter{secnumdepth}{3}
+
+% wallpaper for title page (KIT-Frame)
+\RequirePackage{wallpaper}
+
+% subimport of files
+\RequirePackage{import}
+
+% languages
+\RequirePackage{babel}
+
+% hyphenation for compound words
+\RequirePackage[shortcuts]{extdash}
+
+% nice tables
+\RequirePackage{booktabs}
+\RequirePackage{longtable}
+\RequirePackage{array}
+
+% show graphics in draft mode
+\RequirePackage{graphicx}
+\setkeys{Gin}{draft=false}
+
+% appendix
+\RequirePackage[toc,title,header]{appendix}
+\noappendicestocpagenum
+
+% PDF specific packages
+\RequirePackage[hyphens]{url}
+\RequirePackage[breaklinks,colorlinks=false]{hyperref}
+\newcommand\setpdf{\hypersetup{%
+    pdftitle={\thethesistype},%
+    pdfsubject={\@title},%
+    pdfauthor={\@author},%
+    pdfborder={0 0 0},%
+    }%
+    \let\theauthor\@author
+}

30_Thesis/sections/00_introduction.tex

@@ -0,0 +1,38 @@
+\chapter{Introduction}
+\label{ch:Introduction}
+
+\section{Motivation}
+\label{sec:Introduction:Goals}
+
+Customers and sales people are faced with big catalogues that describe how a complex product can be built. These catalogues specify what is possible to combine but it is easy to make mistakes. The sales process takes a long time because orders have to be manually validated. With the advent of mass customization these issues were addressed. Mass customization uses a configurator. The configurator has a rulebook that contains all the attributes of a product, their corresponding characteristics and rules on how these can be combined. A system like that allows to reduce the workload and cost of sales \cite{shafieeCostBenefitAnalysis2018}. "[...] [P]roduct configuration is seen as a team activity with divergent interests" \cite{mendoncaCollaborativeProductConfiguration2008} and therefore research in the field of group-based configuration starts receiving more attention. 
+To give an idea of situations that can use group-based configuration, here are some examples:
+\begin{itemize}
+    \item A company's lorry fleet (e.g. driver, head of procurement, marketing manager)
+    \item A company's customer management software system (e.g. salesperson, human resource manager, accounting manager)
+    \item A public project to get a new area at a zoo (e.g. visitor, director, zoo-keeper)
+    \item Managing a forest (e.g. owner, environmental protection agency, consumer representative)
+    \item An existing company building and how it should be furnished (e.g. landlord, employee representative, CEO)
+\end{itemize}
+
+Unfortunately, making decisions in a group comes with problems as a lack of communication can lead to worse decision outcomes \cite{atasItemRecommendationUsing2017}. Group dynamics and biases result in suboptimal decisions \cite{kerrBiasJudgmentComparing1996}.
+Generally, group decisions are complex and often the process that yields the decision result is unstructured, thereby not providing any reproducibility of the success. Groups have different power structures and usually individuals have different interests. Moreover, finding solutions is a rather complex task and group decisions can suffer from intransparency.
+
+Group recommenders promise to help with that as they can take individual user preferences and find good compromises for the whole group. They are used in movies, music and travel \cite{garciaGroupRecommenderSystem2009, piliponyte2013sequential, peraGroupRecommenderMovies2013,felfernigGroupRecommenderApplications2018}. The existing literature on recommenders for groups is extensive with many different approaches and domains \cite{delicResearchMethodsGroup2016, chenInterfaceInteractionDesign2011, atasItemRecommendationUsing2017, jamesonRecommendationGroups2007, chenEmpatheticonsDesigningEmotion2014, liuCGSPAComprehensiveGroup2019} but to date there have not been any approaches to combine them with group-based configuration. There have been approaches to combine recommendation techniques with configuration but these were limited to configuration for a single user only \cite{pereiraFeatureBasedPersonalizedRecommender2016, scholzConfigurationbasedRecommenderSystem2017, scholzEffectsDecisionSpace2017}.
+
+
+The above listed examples of use cases for group-based configuration show that ordinary group recommenders cannot be used here as they, unlike configuration, are used for simple products. Configuration on the other hand operates with solutions that are successively built by combining standardized features. This leads to three problems when trying to apply recommenders in this field: first, because of simple combinatorics, the number of variants is enormous. Second, because of the interrelation between features, a small change in configuration can lead to many subsequent changes and, thus, a completely different product. Third, during configuration there is an already pre-existing configuration state that is potentially expensive to change. This could be the current state of a situation for example what a building currently looks like, decisions already made regarding a project that would be expensive to change or simply a part already agreed on at an earlier stage of the project. The group recommender needs to account for these circumstances. Therefore it is necessary to explore new ideas and to take into account knowledge from group recommendation.
+
+\section{Goals of this Thesis}
+\label{sec:Introduction:Goals}
+
+This thesis aims at showing the viability of using group recommenders in a configuration setting by producing and evaluating a prototype. It is discussed what needs to be done to adapt group recommenders to allow usage of the basic recommendation technique of item-based recommendation. The research question for this thesis is the following.
+
+\begin{itemize}
+    \item How can a group recommender translate individual preferences into recommendations that improve the overall satisfaction of group members while considering constraints given by the configuration state? 
+\end{itemize}
+
+
+\section{Structure of this Thesis}
+\label{sec:Introduction:Structure}
+
+This thesis first will give an introduction, then present related work. Next, the concept for a recommender will be presented. Afterwards, design and implementation of the prototype produced in this thesis will be discussed and evaluated. Last, a conclusion will be made which includes a summary and further research possibilities for recommendation techniques in group-based configuration.

30_Thesis/sections/10_foundations.tex

@@ -0,0 +1,228 @@
+\chapter{Foundations}
+\label{ch:Foundations}
+
+This chapter gives an overview of the foundations this thesis is based upon. It introduces product configuration, shows what group based product configuration looks like, examines recommender systems and basic approaches, describes group recommenders, gives a short introduction to the use case and the prototype that is extended by this thesis.
+
+\section{Product Configuration}
+\label{sec:Foundations:ProductConfiguration}
+
+Product configuration is a process consisting of a series of decision tasks whereby a product is constructed of components which interact with each other. During a configuration process no new components are created. Their interplay and specification is defined beforehand \cite[~ pp. 42, 43]{sabinProductConfigurationFrameworksa1998}. This section defines product configuration analogous to \citeauthor{felfernigOpenConfiguration2014} \cite{felfernigOpenConfiguration2014}.
+
+Formally a configuration problem can be specified as a \emph{constraint satisfaction problem (CSP)} \cite{tsangFoundationsConstraintSatisfaction1993} as 
+\begin{equation} \label{eq:Foundations:ProductConfiguration:ConstraintSatisfactionProblem}
+    CSP(V,\mathfrak{D},C),
+\end{equation}
+where $V$ is a set of \emph{variables} (which in this thesis will also be referred to as \emph{features}) with
+\begin{equation} \label{eq:Foundations:ProductConfiguration:Variables}
+    V = \{v_1, \dotsc, v_m\},
+\end{equation}
+a \emph{domain mapping} $\mathfrak{D}$ that maps variable to their corresponding domain values $D$ (which in this thesis will be also referred to as \emph{characteristics}) i.e. the values that can be assigned to each variable
+\begin{equation}\label{eq:Foundations:ProductConfiguration:DomainMapping}
+    \mathfrak{D} : V \to D; x \mapsto \mathfrak{D}(x) \qquad where \ D = \{d_1, \dotsc, d_o\},
+\end{equation}
+and \emph{constraints} $C$ that limit the solution space with 
+\begin{equation}\label{eq:Foundations:ProductConfiguration:Constraints}
+    C = \{c_1, \cdots, c_k\}.
+\end{equation}
+
+In group-based configuration (also known as collaborative or group configuration) a group instead of a single user is set to configure a product. This entails challenges in terms of synchronising  workspaces and keeping the data consistent for every group member. \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}'s \cite{raabKollaborativeProduktkonfigurationEchtzeit2019} approach, which this thesis extends, is to treat the group configuration the same as one shared configuration and to sync the selection of attributes across clients.
+
+\subsection{Group-Based Product Configuration}
+\label{sec:Foundations:GroupBasedProductConfiguration}
+
+Group-based product configuration is an approach to product configuration where product configuration is done by multiple users. According to \citeauthor{felferningGroupBasedConfiguration2016} \cite{felferningGroupBasedConfiguration2016} considering classical approaches as single user tasks can lead to decisions that are suboptimal. Moreover, there are scenarios where a decision is inherently a group task such as \emph{Holiday Planning}, \emph{Software Release Planning} and \emph{Product Line Scoping}. Group-based configuration takes the representation of a group directly into account.
+
+\section{Recommender System}
+\label{sec:Foundations:RecommenderSystem}
+
+A recommender system is a system that gives individualized recommendations to users to guide them through a large space of objects \cite[~ p. 331]{burkeHybridRecommenderSystems2002}.
+
+There are several approaches to recommender systems presented in \cite{felfernigGroupRecommenderSystems2018}, some of them are: collaborative filtering, constraint-based recommendation and content-based filtering. There is also hybrid recommendation which combines approaches but this is not the focus in this thesis.
+
+\subsection{Collaborative Filtering}
+
+\begin{table}[b]
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & The Matrix & Titanic & Die Hard & Forest Gump & Wall-E \\ \hline
+         John  & 5 & 1 &   & 2 & 2 \\
+         Lucy  & 1 & 5 & 2 & 5 & 5 \\
+         Eric  & 2 & ? & 3 & 5 & 4 \\
+         Diane & 4 & 3 & 5 & 3 &   \\
+    \end{tabular}
+    \caption[Movies: Rating Matrix]{An example showing users ratings for movies by \citeauthor{ningComprehensiveSurveyNeighborhoodBased2015} \cite{ningComprehensiveSurveyNeighborhoodBased2015}. Eric would like to know if he will like the movie Titanic.}  
+    \label{tab:Foundations:RecommenderSystem:MoviePreferences}
+\end{table}
+
+In collaborative filtering a user's rating for unknown items is predicted by finding similar users who have rated it. Their rating is used as prediction \cite[~ pp. 7, 8]{felfernigDecisionTasksBasic2018}.
+
+Collaborative Filtering can not only be done using users, it can also be item-based. Hereby the similarity between items is used for a recommendation and not similar users \cite{ricciRecommenderSystemsHandbook2015}.
+
+\autoref{tab:Foundations:RecommenderSystem:MoviePreferences} shows an example rating matrix for movies. A simple user-based way to calculate a rating would be to use a k-nearest neighbour (kNN) algorithm and then take the average of those ratings. Using this method with $k := 2$ and euclidean distance, Eric's closest neighbours are \textit{Lucy} and \textit{Diane} therefore giving a predicted rating of $4$. An item-based approach will try to find similar items based on the user's rating. Here, an example of similar items would be \textit{Forest Gump} and \textit{Wall-E} as John and Lucy each have given them the same rating and Eric's rating is off by one. Using again kNN with $k := 2$ it is found that \textit{Forest Gump} and \textit{Wall-E} are the most similar to \textit{Titanic} thereby having a predicted rating of $4.5$. However this simple similarity and prediction function does not take into account different distances. For example Lucy's ratings are more similar compared to Eric's than Diane's but Diane's and Lucy's ratings are valued the same.
+
+\subsection{Constraint-Based Recommendation}
+Hereby filter rules are defined which filter out items that do not fulfil specified rules. A user models their requirements with these rules and thereby gets a list of recommended items. This approach requires deep knowledge about a product because it requires a detailed description of features  \cite[~ p. 12]{felfernigDecisionTasksBasic2018}.
+
+The previous movie example (see \autoref{tab:Foundations:RecommenderSystem:MoviePreferences}) requires additional information for example about plot structure, pacing, length and other attributes of the movie. Now the user can enter filtering criteria, e.g. that the movie should be no longer than 120 minutes, be categorized as action or thriller and have a fast pacing. The system will only recommend movies that fit into these constraints.
+
+\subsection{Content-Based Filtering}
+For the content-based filtering approach, items and users are assigned to categories. Based on consumption and rating of items a user will have ratings for categories. Predictions are now made based on the categories of a new item \cite[~ pp. 10, 11]{felfernigDecisionTasksBasic2018}.
+
+Using the example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} and using an additional category matrix (see \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}), a rating matrix per category can be derived (using the average rating of the user of each movie contained in this category). The result can be seen in \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}. To predict Eric's rating of Titanic, the categories of \textit{Titanic} and averages of Eric's implicit rating per category are used. Titanic is only in the category romance and as Eric's rating of \textit{Forest Gump} is $5$ the prediction is a rating of $5$. Categories do not have to be the genre, they could be any kind of data relating to a movie.
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & The Matrix & Titanic & Die Hard & Forest Gump & Wall-E \\ \hline
+         Action  & x &  & x  &  &  \\
+         Sci-Fi  & x &  &  &  &  \\
+         Thriller  &  & & x &  &  \\
+         Romance & & x & & x & \\
+         Family & & & & x & x \\
+    \end{tabular}
+    \caption[Movies: Category Matrix]{Showing example categories for the movies in \autoref{tab:Foundations:RecommenderSystem:MoviePreferences}.}
+    
+    \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}
+\end{table}
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & Action & Sci-Fi & Thriller & Romance & Family \\ \hline
+        John  & 5 & 5 & & 1.5 & 2 \\
+        Lucy  & 1.5 & 1 & 2 & 5 & 5 \\
+        Eric  & 2.5 & 2 & 3 & 5 & 4.5 \\
+        Diane & 4.5 & 4 & 5 & 3 & 3  \\
+    \end{tabular}
+    \caption[Movies: User Profile Matrix for Genres]{User profiles generated from categories and ratings from \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} and \autoref{tab:Foundations:RecommenderSystem:ContentBasedFilteringCategories}.}
+    
+    \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}
+\end{table}
+
+Advantages and disadvantages of basic recommendation techniques are listed in \autoref{tab:Foundations:RecommenderComparison}. The following subsections show advantages of content-based filtering over collaborative filtering and over constraint-based recommendation.
+
+\begin{table}[htb]
+    \begin{center}
+        \begin{tabularx}{\columnwidth}{X|X|X}
+            & advantages & disadvantages \\
+            \hline
+            Collaborative Filtering 
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item Serendipity of results 
+                    \item Automatic learning of market segments
+                    \item No domain knowledge required
+                \end{itemize}
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item Cold start problem for users and items
+                    \item Grey sheep problem
+                    \item Quality based on rating quality
+                    \item Data sparsity
+                    \item Privacy not guaranteed
+                \end{itemize} \\
+            \hline
+            Content-Based Filtering 
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item No community required 
+                    \item User-independent
+                    \item Transparent
+                    \item No item cold start
+                    \item Simplicity
+                    \item Robust
+                    \item Stable to constant influx of new users
+                \end{itemize}
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item Overspecialisation
+                    \item No serendipity
+                    \item User cold start problem
+                    \item Requires domain knowledge
+                \end{itemize} \\
+            \hline
+            Constraint-Based Recommendation 
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item Transparent
+                    \item Good for non-discrete values
+                \end{itemize}
+            &   \begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt, leftmargin=3.5mm]
+                    \item Inconsistent constraints
+                    \item No results
+                \end{itemize} \\ 
+        \end{tabularx}
+        \caption[Comparison of Recommendation Approaches]{A description of the advantages and disadvantages of common recommendation techniques \cite{richthammerSituationAwarenessRecommender2018, shokeenStudyFeaturesSocial2019,hahslerRecommenderlabFrameworkDeveloping2015, aminiDiscoveringImpactKnowledge2011, suSurveyCollaborativeFiltering2009}}
+        \label{tab:Foundations:RecommenderComparison}
+    \end{center}
+\end{table}
+
+\subsubsection{Advantages over Collaborative Filtering}
+
+Collaborative filtering has several issues that content-based filtering doesn't have. According to \citeauthor{likaFacingColdStart2014} \cite{likaFacingColdStart2014} the \emph{cold start problem} is one of the well-known problems of recommender systems. It occurs when there is sparse information for users or items. In the case of collaborative filtering this issue concerns for both items and users. Content-based filtering does not have that issue with items as they are classified based on similarity to other items. The user cold start problem however still persists when a new user has not yet rated any items. Accordingly, no similar items can be recommended.
+
+Another common issue is the \emph{grey sheep problem} \cite{grasIdentifyingGreySheep2016}. Collaborative filtering approaches assume that users that are similar, have similar preferences. A user that is not similar to any of the current user or community of users fail that assumption. Therefore, good recommendations cannot be made. These users are called \emph{grey sheep users}. Item-based filtering does not have this issue as a user's preference is directly used to find similar items to the ones they like, not similar users.
+Usually, the need for domain knowledge is a disadvantage. However, as product configuration already has domain knowledge baked in to describe features and how they can be combined, this is not a disadvantage and can even be seen as an advantage. Hence, domain knowledge can directly be used and does not first need to be learned indirectly.
+Additionally, a collaborative filtering approach spans a larger comparison space, based on preferences, compared to content-based filtering that only uses the item attributes. Thus, for applications with a large solution space, reliance on product features instead of user similarity should be considered.
+Last, content-based filtering does not depend on historic group preference accuracy. Thus, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example, this occurs if a user's input into a system does not accurately reflect what they actually like.
+
+\subsubsection{Advantages over Constraint-Based Recommendation}
+
+In constraint-based recommendation approaches it is possible that constraints lead to no possible solution \cite[~ p. 44]{felfernigAlgorithmsGroupRecommendation2018}. This then requires further techniques of constraint relaxing and a user is faced with the situation that they have to search for constraints which fulfil less strict requirements. Moreover, in groups a constraint-based approach has to deal with contrary user constraints. Therefore, diverse groups could have issues with it.
+
+\section{Group Recommender System}
+\label{sec:Foundations:GroupRecommenderSystem}
+
+A group recommender system is a recommender system aimed at making recommendations for a group instead of a single user. To make recommendations preferences of all group members have to be aggregated. This can be done by either aggregating single user recommendations or by merging preferences of each user into a group preference model \cite{jamesonRecommendationGroups2007}. Based on the resulting preference model, recommendation strategies can be used to generate recommendations.
+
+The strategy of aggregating predictions can be further divided into two strategies. \citeauthor{felfernigAlgorithmsGroupRecommendation2018} \cite{felfernigAlgorithmsGroupRecommendation2018} describes merging recommendations and "ranking of candidate items". Merging recommendations can be used when multiple possible solutions are to be presented. The recommender picks $n$ recommendation from each user's individual recommendations and merges them into a list. The second approach is that each user's individual recommender ranks all items. The group member's specific rankings then are aggregated to get a group ranking of items. Instead of ranking it is also possible to simply predict a user's rating for an item.
+
+The aggregation of preferences uses a merging strategy to combine the individual preferences into group preferences. This allows a group to change its preferences during the course of the decision without changing individual preferences.
+
+Both the approach of merging preferences and the approach of using individual user's rankings require some kind of aggregation strategy. Strategies used in this thesis are: multiplication, average and least misery. The multiplication strategy multiplies preferences of users and thereby combines them into a group preference. Similarly the average strategy takes the average of a rating and the least misery strategy takes the lowest rating among group members. To illustrate the example in \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} is used. Lucy, Eric and Diane form a group. The resulting ratings for each strategy are shown in \autoref{tab:Foundations:RecommenderSystem:AggregationStrategy}.
+
+An additional strategy is the dictatorship strategy. When the dictatorship strategy is used a random group member is chosen. This group member is called the dictator. The decision is made only based on the preference of the dictator. It is omitted from the table as the outcome is dependent on random choice.
+
+\begin{table}
+    \centering    
+    \begin{tabular}{ l | c | c | c | c | c }
+        & The Matrix & Titanic & Die Hard & Forest Gump & Wall-E \\ \hline
+         multiplication  & 8 & - & 30 & 75 & - \\
+         average  & $\frac{7}{3}$ & - & $\frac{10}{3}$ & $\frac{13}{3}$ & - \\
+         least misery  & 1 & - & 2 & 3 & - \\
+    \end{tabular}
+    \caption[Movies: Result Matrix for Group Aggregation Strategies]{An example showing preference aggregation strategies for a group using the data from \autoref{tab:Foundations:RecommenderSystem:MoviePreferences}. Titanic and Wall-E were left out because not all group members have rated these movies.}
+    \label{tab:Foundations:RecommenderSystem:AggregationStrategy}
+\end{table}
+
+
+\section{Use Case}
+\label{sec:Foundations:UseSystem}
+
+This thesis presents one use case from forestry. It gradually extends this use case wherever needed (see \autoref{subsec:Concept:ReccomendationGeneration:PreferenceScoring}, \autoref{sec:Concept:Illustration} and \autoref{sec:Evaluation:UseCase}) which is why it is not presented in much detail here. The product that is configured is a forest and it has certain features. These range from the amount of trees that are indigenous, climate resilient, or usable to produce wood to the quantity that should be harvested. 
+
+\section{Base System}
+\label{sec:Foundations:BaseSystem}
+
+This thesis extends a base software system, \emph{CAS Configurator Merlin}, from \emph{CAS Software AG} \cite{CASSoftwareAG}. \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} \cite{raabKollaborativeProduktkonfigurationEchtzeit2019} extends CAS Merlin Configurator in his thesis to allow simultaneous configuration. Here groups of people are able to simultaneously configure a product. If there are any conflicts, a conflict resolution process is started. However the prototype only allows a majority voting approach and does not provide any further group decision support. Also this process only starts upon the selection of an invalid state that gives alternatives and for the main configuration process no recommendation or conflict resolution exists.
+
+The extended architecture is shown in \autoref{fig:Foundations:CollaborativeConfiguratorMerlin}.
+\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} only makes changes to M.Customer which is renamed to \texttt{M.Collab-Customer} and introduces a new component \texttt{M.Collab}.
+
+The following list provides a short overview of each component.
+
+\begin{description}
+    \item[\texttt{M.Core}] provides the base of the configurator. It checks the configuration against all rules in the database, provides possible alternatives if a change invalidates other parts of a configuration. The system relies on a CSP solver for validation and suggestion of alternatives.
+    \item[\texttt{M.Model}] is the editor to create products and rules. These rules can then be uploaded to \texttt{M.Core} .
+    \item[\texttt{M.Collab}] is a node.js server application that communicates with \texttt{M.Core} via REST-API and with \texttt{\texttt{M.Collab-Customer}} via WebSockets. It sits in between \texttt{M.Collab-Customer} and \texttt{M.Core} and handles all processing regarding collaborative configuration.
+    \item[\texttt{M.Collab-Customer}] is the customer facing component. It allows a customer to configure a product or solution and contains the user interface. Actions undertaken in \texttt{M.Collab-Customer} are send to \texttt{M.Collab}.
+\end{description}
+
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.6\textwidth]{./figures/20_fountations/MerlinCollaborativeConfigurator.pdf}
+    \caption[Architecture: Collaborative Configurator]{Architecture of Collaborative Configurator Merlin \cite[Fig. 4.3]{raabKollaborativeProduktkonfigurationEchtzeit2019}}
+    \label{fig:Foundations:CollaborativeConfiguratorMerlin}
+\end{figure}
+
+\section{Software Design}
+\label{sec:Foundations:BaseSystem}
+
+In this thesis for the design of the prototype to improve software quality some rules of software engineering will be applied. The usage of \emph{design patterns} \cite{gamma2015design} and the rules of \emph{separation of concern} \cite{de2002importance} and \emph{single responsibility} \cite{martinCleanArchitectureCraftsman2017} are commonly known in the field of software engineering and, therefore, a detailed explanation is omitted here.
+
+

30_Thesis/sections/20_related_work.tex

@@ -0,0 +1,37 @@
+\chapter{Related Work}
+\label{ch:Related_Work}
+
+This chapter presents approaches for group recommenders, group-based configuration systems and recommender systems for configuration. However no literature combines these approaches to get a group recommendation for group-based configuration.
+
+\section{Group Recommender Systems}
+\label{sec:Related_Work:GroupRecommender}
+
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{choudharyMulticriteriaGroupRecommender2020} \cite{choudharyMulticriteriaGroupRecommender2020}] propose a multi-criteria group recommender system. An analytical hierarchy process is used to learn priorities for film features like story, action, direction and to then make a number of best recommendations for a group of users. Their approach works well for film selection and they observe that it is easier to make recommendations for homogenous groups than for random groups. Also small groups receive better recommendations compared to large ones.
+
+    \item[\citeauthor{chenInterfaceInteractionDesign2011} \cite{chenInterfaceInteractionDesign2011}] looks at interface and interaction designs that support the overall group and does not only consider each user individually. Chen designs a music recommendation system \emph{GroupFun} with a focus on groups that tries to enhance mutual awareness and transparency. \citeauthor{chenInterfaceInteractionDesign2011}'s assessment is that this work is still in a preliminary stage. Further work in that area was conducted by \citeauthor{chenEmpatheticonsDesigningEmotion2014} \cite{chenEmpatheticonsDesigningEmotion2014} looking at emotional awareness in groups and how this can be visualised in a user interface. 
+\end{description}
+
+\section{Group-Based Configuration}
+\label{sec:Related_Work:GroupBasedConfiguration}
+
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{felferningGroupBasedConfiguration2016} \cite{felferningGroupBasedConfiguration2016}] introduce basic definitions of group-based configuration task, show what conflicts can occur, how to deal with inconsistencies in preferences among group members and how to integrate different decision heuristics into this process.
+
+    \item[\citeauthor{velasquez-guevaraMultiSPLOTSupportingMultiuser2018} \cite{velasquez-guevaraMultiSPLOTSupportingMultiuser2018}] implement a web-based simultaneous group-based configuration system that uses constraints. Hereby each user configures according to their own preferences and the system proposes a configuration according to different strategies. These strategies are maximization of selections (which means finding the solution with the largest number of features selected), minimizing conflicting selections (which means to minimize the inclusion of features that have conflicts) and to prioritize decisions from some users.
+\end{description}
+
+\section{Recommender Systems for Configuration}
+\label{sec:Related_Work:RecommenderSystemsForConfiguration}
+\begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{falknerRecommendationTechnologiesConfigurable2011} \cite{falknerRecommendationTechnologiesConfigurable2011}] provide an overview of recommendation approaches for configuration to improve usability of configuration systems. They look at feature recommender to recommend which features in a configuration would be useful to have and at value recommender for these features. Additionally they discuss approaches for ranking and recommending explanations for inconsistencies between customers requirements and product rules however they do not provide any recommendations and point towards further needed research.
+      
+    \item[\citeauthor{rubinshteynEntwicklungHybridenRecommender2018} \cite{rubinshteynEntwicklungHybridenRecommender2018}] looks at different approaches to recommendation and implements a prototype with CAS Merlin Configurator which uses a hybrid recommender system. His prototype combines constraint-based filtering with collaborative filtering and achieves better results in terms of precision than non-hybrid systems using collaborative filtering and constraint-based recommendation. In terms of recall his hybrid does not surpass the high numbers of constraint-based recommendations but improves upon collaborative-filtering. 
+
+    \item [\citeauthor{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017} \cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}] uses a constraint based recommender that uses fuzzy logic to relax constraints and thereby reducing the amount of times where the recommender is unable to make recommendations. With his approach a product manager has direct influence on the recommendations. Rules for recommendations hereby are not automatically learned but only manually created and relate to predefined user interest categories.
+
+    \item [\citeauthor{ullmannEntwurfUndUmsetzung2017} \cite{ullmannEntwurfUndUmsetzung2017}] implements a recommendation engine that is able to estimate customer budgets, a k-nearest neighbour classifier for finding a base configuration and non-negative matrix factorization combined with nearest neighbour to find configurations for specific users. \par
+
+    \item[\citeauthor{wetzelPersonalisierterUndLernender2017} \cite{wetzelPersonalisierterUndLernender2017}] combines collaborative filtering and click-stream analysis. For collaborative filtering he implements three filtering algorithms: k-nearest neighbour, weighted majority voting and non-negative matrix factorization. Collaborative filtering is used to find configurations that are similar to the current configuration. Click-stream analysis is done by using n-grams and the Smith-Waterman algorithm. \citeauthor{wetzelPersonalisierterUndLernender2017} also tries to use click-stream data in combination with Markov chains to give recommendations on how configuration options should be ordered in a configuration form but click-streams do not yield any improvements in terms of accuracy and recall.
+\end{description}
+

30_Thesis/sections/40_concept.tex

@@ -0,0 +1,301 @@
+\chapter{Concept}
+\label{ch:Concept}
+
+In this chapter definitions from \autoref{ch:Foundations} are extended, requirements formulated and assumptions made. Later, user interaction with the recommender system is described and the case study presented. Moreover, the process of generating recommendations is formulated and described. Last, the concept is illustrated using an example.
+
+\section{Extension of Foundations}
+\label{sec:Concept:ExtensionOfFoundations}
+
+The definitions described in \autoref{ch:Foundations} need to be extended for this thesis. This section adds definitions that are required.
+
+\subsection{Configuration State}
+
+A \emph{configuration} $S$ will be defined as a tuple of variables (\autoref{eq:Foundations:ProductConfiguration:Variables}) and their corresponding domain value with
+\begin{equation} \label{eq:Foundations:ProductConfiguration:ConfigurationState}
+    S = \{ (v_i,\ d) \ |\ v_i \in V \ \land \ d \in \mathfrak{D}(i),\ i=1,\dotsc,m \}.
+\end{equation}
+Essentially it is a set of variables and assigned values.
+
+\subsection{Finished Configuration}
+To define what a \emph{finished configuration} is, it is necessary to first define what it means for a configuration to be valid. Therefore $is\_valid$ is defined as
+\begin{equation} \label{eq:Foundations:ProductConfiguration:IsValid}
+    is\_valid : S \to \{true, false\}; x \mapsto 
+    \begin{cases}
+        true, & S \in solution\_space \\
+        false, & \text{otherwise}
+    \end{cases},
+\end{equation}
+with $solution\_space$ being the solution space of the corresponding constraint satisfaction problem. A \emph{finished configuration} $S_F$ is a configuration that contains all variables and is a valid configuration:
+\begin{equation} \label{eq:Foundations:ProductConfiguration:FinishedConfiguration}
+    S_F \subset S,\ where \ \forall v_i \in V (\exists (v_i, d) \in S_F : d \in \mathfrak{D}(i)) \land is\_valid(S_F).
+\end{equation}
+In practice, a finished configuration of a product is something that is ready to be produced. For example if a car is being configured, this means that the car can be produced in the specified way given by the finished configuration.
+
+
+\subsection{Group-Based Product Configuration}
+\label{sec:Foundations:GroupBasedProductConfiguration}
+
+Instead of a single person configuring a product, a group of people configures one product which can be useful in multi-stakeholder decisions. This setting needs mechanisms for describing the preferences of multiple people. Thus, a set of users $U$ will be introduced with
+\begin{equation}\label{eq:Foundations:ProductConfiguration:Users}
+    U = \{1, \dotsc, n\},
+\end{equation}
+and a user's \emph{utility function} that maps a domain value to a utility value and is only known to the user
+\begin{equation}
+    \begin{split}
+        u_i(d_j), \qquad \text{where}\ & u_i(d_j) \in [0,1] \\
+        & d_j \in  \mathfrak{D}(j),\\
+        & 1 \leq j \leq m, \\
+        & 1 \leq i \leq n.
+    \end{split}
+\end{equation}
+
+\subsection{Group Recommender}
+
+For a group recommender system additional definitions are required. The attitude of users is represented by their preferences $P$ which are directly related to how the user benefits from a domain value being present in the configuration. Let 
+\begin{gather} \label{tab:Foundations:GroupRecommenderSystem:Preferences}
+    P = \{ P_1, \dotsc, P_n\},\ \text{where} \\
+    P_i = \{(d,\ u_i(d)) \ | \ \forall d \in \mathfrak{D}(i),\ i=1,\dotsc,m \} \notag
+\end{gather}
+
+
+\section{Requirements}
+\label{sec:Concept:Requirements}
+
+This section lists requirements that are considered and implemented in this thesis by the group-based configurator, including the recommendation system.
+
+\begin{itemize}
+    \item Mandatory:
+        \begin{itemize}
+            \item The recommender should support a continuous value range for preferences
+            \item The recommendation engine can be used without proprietary software.
+            \item Give recommendations to a group, based on their preferences and the current configuration state.
+            \item The system supports multiple group configurators at the same time.
+            \item The system should take the current configuration state into account.
+
+            \item Recommendations should always be valid solutions.
+            \item The system has to respond in a timely manner.
+        \end{itemize}
+    \item Optional:
+        \begin{itemize}
+            \item Provide a simple user interface.
+            \item The system should be able to work with other configuration systems.
+            \item Recommendations should allow different scoring functions.
+        \end{itemize}
+\end{itemize}
+
+
+\section{Assumptions}
+\label{sec:Concept:Assumptions}
+
+Due to the fact that a thesis has limited resources, some assumptions have to be made. The assumptions made are listed in this section.
+
+\begin{itemize}
+    \item Only one product/solution is supposed to be configured at the same time by one group.
+    \item Features only support single value attributes.
+    \item The configuration is started after all group members are present.
+    \item Speed and optimization of the system does not have high priority.
+    \item The user interface is for demo purposes.
+\end{itemize}
+In order to reduce complexity, the assumptions are made that only single value attributes are supported and one product is configured at a time by a group. Consequently, less implementation work is needed. However, results are not dependent on these implementations. Most features use only single value attributes. It is also possible to model a feature that supports multiple attributes. As an example the feature \emph{audio system} has the following options: \emph{Bluetooth} or \emph{CD}. To allow multiple attributes to be selected the characteristics \emph{Bluetooth and CD} can be added.
+The assumption that users join the system and only start configuring once all members of the group joined is made to reduce the amount of work needed for creating a lobby and waiting system which is not relevant for showing the functionality of the recommender. Speed and optimization of the system having no high priority as the speed has no influence on the results of this thesis. Therefore, as long as the system is fast enough, no further optimisation of speed is needed.
+The evaluation is done solely with the recommender system and its APIs. Thus, the user interface is not directly relevant for the evaluation but it is relevant for communication of results.
+
+\section{Case Study}
+\label{sec:Concept:CaseStudy}
+
+The case study used in this thesis is a simplified version from forestry.
+The used characteristics and attributes are shown in \autoref{fig:Concept:ForestExample}. Additionally, as examples preferences, a configuration state and a finished configuration are given.
+
+\newpage
+
+\section{User Interaction with the System}
+\label{sec:Concept:UserSystemInteraction}
+
+There is one main way to use the system which is defined in \autoref{tab:Concept:MainUseCase}. This process is also visualized in \autoref{fig:Concept:ConfigurationProcess}. All users start the configuration process together.
+They select the current state at the beginning of the process. Then users repeatedly enter their preferences until each user's preferences have been entered into the system. Based on the entered preferences the system generates a recommendation that is a compromise of all users preferences.
+
+\begin{table}[htb]
+    \begin{center}
+        \begin{tabularx}{\columnwidth}{l|X}
+            \multicolumn{2}{c}{Main System Usage} \\
+            \hline
+            Preconditions   & 
+                \begin{itemize}
+                    \item The configurator is opened on each of the group member's machines
+                    \item The configuration is in an unfinished state (this state is a consensus state)
+                \end{itemize} \\
+            \hline
+            Postcondition   & 
+                \begin{itemize}
+                    \item All users have entered their preferences for each attribute explicitly.
+                    \item The system gives a recommendation based on all preferences and the unfinished configuration state
+                \end{itemize} \\
+            \hline
+            Basic Flow      & 
+                \begin{enumerate}
+                    \item A user indicates a preference for an attribute
+                    \item The system generates a recommendation (based on preferences and configuration status)
+                    \item If not all users have given their preferences go to step 1.
+                \end{enumerate} \\
+            \hline
+        \end{tabularx}
+        \caption[Main System Usage]{A description of the main way users will interact with the system}
+        \label{tab:Concept:MainUseCase}
+    \end{center}
+\end{table}
+
+\begin{figure}[htb]
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/40_concept/bpmn_configuration_process_with_continious_recommendation.pdf}
+    \caption[Configuration Process]{A BPMN diagram of the configuration process.}
+    \label{fig:Concept:ConfigurationProcess}
+\end{figure}
+
+\section{Recommendation Generation}
+\label{sec:Concept:SolutionGeneration}
+
+This section describes how recommendations are generated. The recommender system has a database that stores possible finished configurations and the goal is to rank these recommendations according to a scoring function and to recommend the best possible configuration. The scoring function is referred to as the \emph{group configuration scoring function}. It uses the current configuration state, the preferences of the group and a finished configuration to calculate a score. This score resembles how good this configuration resembles the interest of the group. The exact procedure looks as follows:
+
+\begin{enumerate}
+    \item Assign a score to each stored configuration according to $$score_{group}(\overline{configurationState},\ \overline{preferences}, \ configurationInStore)$$
+    \item Chose the configuration with the highest score as recommendation.
+\end{enumerate}
+
+It is optionally possible to have multiple runs with different scoring functions. This, for example, allows the removal of configurations that cause a lot of misery.
+
+
+
+\subsection{Scoring Function}
+
+\label{subsec:Concept:SolutionGeneration:ScoringFunction}
+
+The \emph{group configuration scoring function} includes preferences and current configuration state. This function gives a score for a finished configuration (while using the current configuration state and all user preferences):
+\begin{equation}
+    score_{group}: S \times P \times S_F \to \mathbb{R}
+\end{equation}
+
+An example group configuration scoring function is $score_{group}$ defined as
+\begin{equation}
+    score_{group}(\overline{s},\ \overline{p},\ s) \coloneqq score(\overline{p},\ s) \cdot penalty(\overline{s},\ s)
+\end{equation} 
+
+This thesis will use multiple scoring functions. Among those are ones for least misery, average and multiplicative which all are implemented by $score$ (see \ref{subsec:Concept:ReccomendationGeneration:PreferenceScoring} and \ref{subsec:Concept:ReccomendationGeneration:Penalty}). Average and multiplicative yield good results among the studies presented by \citeauthor{Masthoff2015} \cite{Masthoff2015}. Strategies can also be combined, one example here is average without misery. The scoring functions used for this thesis all combine $penalty$ and $score$ by multiplication. However it is possible to use other combination strategies and it is possible to combine multiple scoring functions into one group scoring function. This thesis will use simpler scoring functions that are not combined but improvement here is possible.
+
+\subsection{Preference Scoring}
+\label{subsec:Concept:ReccomendationGeneration:PreferenceScoring}
+
+All of the aggregation functions mentioned in \autoref{subsec:Concept:SolutionGeneration:ScoringFunction} have one preference per product. For configuration where a preference for all characteristics exists there needs to be a function that combines the preferences of one user into her configuration score. After one score has been calculated per user the mentioned preference aggregation strategies can be used.
+
+A simple scoring function approach is to use the preference for each characteristic that is part of the configuration and then use the average. This approach is transparent because the preference of a user is directly translated into the score and no weighting is done. It means that a configuration score is simple to understand and to calculate. However, if needed, for example, to give one group member more power, it allows relative weighting. This can be done with preprocessing of preferences. Moreover, an approach like this ensures that through preprocessing feature weights can be added. It is therefore possible that a user gives different importances to features. Also, other means of weighting ratings are possible. For example the ratings of one group member who has more knowledge in an area can be increased by multiplication with a factor or alternatively the preferences for all other users can be decreased.
+The formula for this rating function looks as follows:
+
+\begin{equation}
+    score(\overline{p},\ s) = aggr( \ \{score_{user}(P_i, s) \ | \ P_i \in \overline{p} \} \ )
+\end{equation}
+where $aggr$ is the aggregation function and $score_{user}(P_i, s)$ is the configuration score of user $i$ which is defined as
+\begin{equation}
+    score_{user}(P_i, s) = average(\{x \ | \ (characteristic, x) \in P_i \land characteristic \in s \}) \notag . 
+\end{equation}
+
+
+\begin{figure}[htb]
+    \begin{mdframed}[
+        nobreak=true,
+        frametitle={Example for Forestry Use Case},
+        linecolor=black, 
+        frametitlerulecolor=black, 
+        frametitlebackgroundcolor=gray!5
+    ]
+        In this example there is a small group of users. The use case is a piece of forest and variables are for example harvesting activity, which trees to grow and accessibility for people.
+        \begin{align}
+            \begin{split}
+                V = \{ & \textit{indigenous}, \textit{resilient}, \textit{usable}, \textit{effort}, \textit{quantity}, \textit{price}, \textit{accessibility} \},
+            \end{split} \notag \\
+            \mathfrak{D}(\textit{indigenous}) =  \{ & \text{low}, \text{moderate}, \text{high}\}, \notag \\
+            \mathfrak{D}(\textit{resilient}) = \{ & \text{low}, \text{moderate}, \text{high}\}, \notag \\
+            \mathfrak{D}(\textit{usable}) = \{ & \text{low}, \text{moderate}, \text{high}\}, \notag \\
+            \mathfrak{D}(\textit{effort}) = \{ & \text{manual}, \text{harvester}, \text{autonomous}\}, \notag \\
+            \mathfrak{D}(\textit{quantity}) = \{ & \text{low}, \text{moderate}, \text{high}\}, \notag \\
+            \mathfrak{D}(\textit{price}) = \{ & \text{low}, \text{moderate}, \text{high}\}, \notag\\
+            \mathfrak{D}(\textit{accessibility}) = \{ & \text{low}, \text{moderate}, \text{high}\},\notag \\
+            U = \{ & 1,2\} \notag\\
+            P = \{ & P_1, P_2\} \notag\\
+            \begin{split}
+                P_1 = \{ & (\text{manual}, 0.8), (\text{harvester}, 0.3) \} \\ 
+                & \cup \{ (d,0.5)\ |\ d \in \mathfrak{D}(i),\ i \in V,\ i \notin \{ \text{manual}, \text{harvester}\} \ \} \ 
+            \end{split} \notag \\
+            P_2 = \{ & (d,0.5)\ |\ d \in \mathfrak{D}(i),\ i \in V \} \notag \\
+            S  =  \{ & (\textit{indigenous}, \text{low}), (\textit{quantity}, \text{moderate}) \} \notag \\
+            \begin{split}
+            S_F  =  \{ & (\textit{indigenous}, \text{low}), (\textit{resilient}, \text{low}), (\textit{usable},\text{low}), (\textit{effort}, \text{manual}), \\
+            & (\textit{quantity}, \text{low}), (\textit{price},\text{high}),(\textit{accessibility}, \text{low}) \} 
+            \end{split} \notag
+        \end{align}
+    \end{mdframed}
+    \caption[Forestry Use Case]{An example of use case in forestry that includes two people.}
+    \label{fig:Concept:ForestExample}
+\end{figure}
+
+The example in \autoref{fig:Concept:ForestExample} contains two users. The first user has preferences for the characteristic \emph{manual} of the feature with $0.8$ and the characteristic \emph{harvester} of the same feature with $0.3$. All other characteristics have a preference of $0.5$. The second user's preferences are $0.5$ for all characteristics. The finished configuration that is supposed to be rated in this example contains the characteristics \emph{low} for each feature except for \emph{effort} and \emph{quantity} which are set to \emph{manual} and \emph{high}. The score fore the finished configuration $S_F$ of user one is $0.54$. This score is the average of all seven features. User one rates all characteristics of all features as $0.5$ except two characteristics for \emph{effort}. Thus, all feature scores for this user are $0.5$ except the score for \emph{effort} is $0.8$ because of the user's preference of $0.8$ for the characteristic \emph{manual}. The resulting average score per feature of $0.54$ is the user's score for this configuration. User two rates all characteristics with $0.5$ therefore the resulting average is $0.5$.
+The group configuration score is dependent on the used aggregation strategy. Multiplication results in a score of $0.54 \cdot 0.5 = 0.27$. The score for average is $\frac{1}{2}(0.54 + 0.5) = 0.52$ and for least misery $\min \{0.54, 0.5\} = 0.5$.
+
+\subsection{Configuration Change Penalty}
+\label{subsec:Concept:ReccomendationGeneration:Penalty}
+
+This thesis proposes the following penalty function which gives the percentage of characteristics that exist in the configuration that is to be rated. This value can be tuned to be more or less strict by potentiating. This is done by selecting different values for $\alpha$, thereby, allowing more deviation or less deviation from the current configuration state. The penalty function is defined as
+\begin{equation}
+    \notag \alpha \in \mathbb{R}, \qquad     unchanged(d,\overline{s}, s) = 
+    \begin{cases}
+      1, & d \in \overline{s} \land d \in s \\
+      0, & \text{otherwise}
+    \end{cases}
+\end{equation}
+
+\begin{equation}
+    penalty_{proportion}(\overline{s},\ s) =  \left(\frac{\sum_{d \in \overline{s}} unchanged(d,\overline{s}, s)}{|\overline{s}|}\right)^\alpha.
+\end{equation}
+In essence the function checks the number of unchanged characteristics and divides this by the number of characteristics that are in the current configuration state. The result is the proportion of unchanged characteristics when comparing the current configuration state to the finished configuration.
+
+By including the current configuration state, the scoring function can take into account that some characteristics have already been realized and accordingly might be very costly to change. A higher $\alpha$ resembles a higher cost of change and an alpha of zero represents no costs for changes.
+
+\section{Illustration}
+\label{sec:Concept:Illustration}
+
+This section gives an example to illustrate how the recommendation works. The example in \autoref{fig:Concept:ForestExample} is used for that but the preferences are extended (see \autoref{tab:Concept:UseCaseRating}). \autoref{tab:Concept:UseCaseConfigurations} shows the current configuration state which consists of the characteristic moderate for the feature \textit{indigenous} and  \textit{resilient} respectively. $S_{F1}$ to $S_{F4}$ show the stored configurations for this example. The features that will be focused on are \textit{indigenous}, \textit{resilient} and \textit{effort}. In the presented example $S_{F1}$ performs best. The exact reason for that will be presented here. $S_{F1}$ is compared to $S_{F2}$ to show the effect of divergence from the configuration state.  A comparison between $S_{F1}$  and $S_{F3}$ is done to show the difference between preferences and the effect on the score and last, $S_{F4}$ is done to show the effect of switching to better preferences but diverging from the current state. The configurations all differ from $S_{F1}$ in only one characteristic that is chosen differently. As aggregation strategy the \emph{average} metric is used (see \autoref{sec:Foundations:GroupRecommenderSystem}). The parameter $\alpha$ (see \autoref{subsec:Concept:ReccomendationGeneration:Penalty}) is set to 1. A lower $\alpha$ reduces the penalty given to configurations that deviate from the configuration state $S$ and a higher $\alpha$ increase the reluctance to change.
+
+\begin{table}[htb]
+    \tiny
+    \begin{tabularx}{\columnwidth}{C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|}
+        & \multicolumn{3}{c|}{\textit{indigenous}} & \multicolumn{3}{c|}{\textit{resilient}} & \multicolumn{3}{c|}{\textit{usable}} & \multicolumn{3}{c|}{\textit{effort}} & \multicolumn{3}{c|}{\textit{quantity}} & \multicolumn{3}{c|}{\textit{price}} & \multicolumn{3}{c|}{\textit{accessibility}} \\
+        \rotatebox[origin=c]{90}{\ preferences} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{manual} & \rotatebox[origin=c]{90}{harvester} & \rotatebox[origin=c]{90}{autonomous} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} \\
+        \hline
+        $P_1$   & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & \textbf{0.1} & \textbf{0.7} & \textbf{0.9} & \textbf{0.6} & \textbf{0.8} & \textbf{0.2} & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\
+        $P_2$   & \textbf{0.1} & \textbf{0.6} & \textbf{0.9} & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & \textbf{0.8} & \textbf{0.3} & \textbf{0.1} & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\
+    \end{tabularx}
+    \caption[Forestry Use Case: Example Preferences]{A table showing the preferences of an example for this section.}
+    \label{tab:Concept:UseCaseRating}
+\end{table}
+
+\begin{table}[htb]
+    \tiny
+    \begin{tabularx}{\columnwidth}{C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|}
+        & \multicolumn{3}{c|}{\textit{indigenous}} & \multicolumn{3}{c|}{\textit{resilient}} & \multicolumn{3}{c|}{\textit{usable}} & \multicolumn{3}{c|}{\textit{effort}} & \multicolumn{3}{c|}{\textit{quantity}} & \multicolumn{3}{c|}{\textit{price}} & \multicolumn{3}{c|}{\textit{accessibility}} \\
+        \rotatebox[origin=c]{90}{\ configuration} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{manual} & \rotatebox[origin=c]{90}{harvester} & \rotatebox[origin=c]{90}{autonomous} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} \\
+        \hline
+        $S_{\ \ }$         & - & \pmb{\checkmark} & - & - & \pmb{\checkmark} & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
+        $S_{F1}$    & - & \pmb{\checkmark} & - & - & \pmb{\checkmark} & - & - & \checkmark & - & \pmb{\checkmark} & - & - & - & - & \checkmark & - & \checkmark & - & \checkmark & - & - \\
+        $S_{F2}$    & - & \pmb{\checkmark} & - & - & - & \pmb{\checkmark} & - & \checkmark & - & \pmb{\checkmark} & - & - & - & - & \checkmark & - & \checkmark & - & \checkmark & - & - \\
+        $S_{F3}$    & - & \pmb{\checkmark} & - & - & \pmb{\checkmark} & - & - & \checkmark & - & - & \pmb{\checkmark} & - & - & - & \checkmark & - & \checkmark & - & \checkmark & - & - \\
+        $S_{F4}$    & - & - & \pmb{\checkmark} & - & \pmb{\checkmark} & - & - & \checkmark & - & \pmb{\checkmark} & - & - & - & - & \checkmark & - & \checkmark & - & \checkmark & - & - \\
+    \end{tabularx}
+    \caption[Forestry Use Case: Example Configurations]{The current configuration state $ S $ and the stored finished configurations $ S_{Fi} $.}
+    \label{tab:Concept:UseCaseConfigurations}
+\end{table}
+
+The difference between  $S_{F1}$ and  $S_{F2}$ is that instead of containing \emph{moderate} for the feature \emph{resilient} $S_{F2}$ contains \emph{high}. The scores for these two characteristics are the same, with a value of $0.55$, as both users have rated them at $0.5$ but since $S_{F2}$ deviates from the configuration state there will be a penalty. There are two characteristics in the configuration state $S$, therefore, the penalty is $(\frac{1}{2})^\alpha = (\frac{1}{2})^1 = 0.5$. This means the score of $S_{F2}$ is half of $S_{F1}$, resulting in a final score of $0.275$ compared to $0.55$.
+
+The only difference between $S_{F1}$ and $S_{F3}$ is that $S_{F3}$ changes the selection for the feature \emph{effort}. The characteristic \emph{manual} is chosen in $S_{F1}$ and the characteristic \emph{harvester} for $S_{F3}$. The individual score for user one increases as they prefer \emph{harvester} with $0.8$ over \emph{manual} with $0.6$. However, user two has an individual score reduction as their score changes from $0.8$ for \emph{manual} to $0.3$ for \emph{harvester}. The larger decrease in the score of user two causes a decrease in the overall score when comparing  $S_{F1}$ to $S_{F3}$ with a score of $0.55$ to $0.53$. The scores for both users are closer together for $S_{F1}$. However, this does not necessarily have to be the case if the preference of user two for harvester were to change to $0.6$ because then both configurations would have the same score. A different user preference aggregation strategy can change that.
+
+Last, $S_{F1}$ and $S_{F4}$ differentiate in terms of the characteristic choice for the feature \emph{indigenous}. The switch from \emph{moderate} to \emph{high} when changing from $S_{F1}$ to $S_{F4}$ causes an increase in the individual scoring function of user two. This is caused because her preference for \emph{moderate} is $0.6$ and for \emph{high} is $0.9$. This results in a score of $0.57$ for $S_{F4}$. Yet, the change that causes the preference scoring function to give a higher score entails a penalty as the characteristic \emph{high} is not part of the configuration state. This penalty causes the overall score to drop to $0.29$ compared to the score of $S_{F1}$ with $0.55$.
+
+ 

30_Thesis/sections/50_design_and_implementation.tex

@@ -0,0 +1,97 @@
+\chapter{Design and Implementation}
+\label{ch:DesignImplementation}
+
+This thesis requires a group configuration system with a recommender. In this thesis a modified version of \emph{CAS Configurator Merlin} \cite{IndustrySpecificProduct2020} is used that has been developed by \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}. The base system is described in \autoref{sec:Foundations:BaseSystem}.
+
+For this thesis \texttt{M.Collab} and \texttt{M.Collab-Customer} have undergone some slight modifications to allow the displaying of recommendations and to query the recommender. The user interface is shown in \autoref{fig:DesignImplementation:UserInterface}. It is extended by adding a preference slider that works in the interval $[0,1]$. The user receives feedback on what a value means based on the thumb position. The thumb rotates from thumbs-down to neutral to thumbs-up based on the selected value. Also functionality to show other participants in the session was added.
+\begin{figure}[htb]
+    \centering
+    \includegraphics[width=0.8\textwidth]{./figures/50_design_and_implementation/user_interface_prototype.png}
+    \caption[User Interface]{The user interface of the prototype, seen by one user while configuring with three other people. The circles in the top left represent the users that are connected in the current sessions. The current configuration state is shown by the characteristics coloured in red. Recommendations are surrounded with a green border.}
+    \label{fig:DesignImplementation:UserInterface}
+\end{figure}
+
+
+\section{Microservice}
+\label{sec:DesignImplementation:Microservice}
+
+An existing base system can be extended in different directions. As \texttt{M.Collab} already functions as middleman between \texttt{M.Collab-Customer} and \texttt{M.Core} , one solution would be to add recommender functionality to \texttt{M.Collab-Customer}. However, this approach would conflict with the \emph{single responsibility} \cite{martinCleanArchitectureCraftsman2017} and \emph{separation of concern} \cite{de2002importance} software design principles. It would change \texttt{M.Collab} from a system that works similar to a router to one that additionally handles  recommendations. \texttt{M.Collab} manages communication between \texttt{M.Collab-Customer} instances and between \texttt{M.Core} and \texttt{M.Collab-Customer}. Thus this solution was discarded.
+
+Another viable solution is adding the recommendation functionality to \texttt{M.Core}. The main benefit of this approach is that it allows the recommender to use systems that are used for solving constraint satisfaction problems to enhance recommendations. The disadvantage of this approach is, however, that now the recommender is tightly coupled with the configurator. Not only does this mean that reproducing results in this thesis is only possible with access to a non-open-source product but it also means that there is no possibility to use the recommender for other configuration solutions. Moreover, no plugin API exists and, as a result the extension would require a large effort.
+
+
+\begin{figure}[tb]
+    \centering
+    \includegraphics[width=0.55\textwidth]{./figures/50_design_and_implementation/MerlinCollabRecommender.pdf}
+    \caption[Architecture: Recommender System and Configurator]{The architecture of the extended recommender system based on \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}'s configurator (see \autoref{fig:Foundations:CollaborativeConfiguratorMerlin}). The changes include the addition of the component \texttt{M.Recommend} and the recommender database. Changes are marked in grey. Components that have a grey background are added and components with a partial grey background undergo changes.}
+    \label{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}
+\end{figure}
+
+Due to these reasons it was decided to create a new microservice (see \autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}), called \texttt{M.Recommend} which communicates with \texttt{M.Collab}, and thereby it is loosely coupled with other system components. Additionally, this approach allows to add functionality that takes  advantage of a constraint satisfaction problem solver by either communicating with one via an API or by integrating one directly into the recommender component \texttt{M.Recommend}. Moreover, it also allows the use of different technologies instead of JavaScript and Node.js. Using a separate component also allows to scale the system differently as it is possible to use multiple recommender instances for one configurator instance or one recommender instance with multiple configurator instances. This is an advantage in the age of automatically scaling systems.
+
+
+\section{API}
+\label{sec:DesignImplementation:API}
+
+The API used for the recommender is a REST-API. REST allows the flexible addition of new endpoints thereby making the system easy to extend.
+When a recommendation is supposed to be generated, \texttt{M.Collab} sends a HTTP-POST-request to M.Recommend (see \autoref{fig:DesignImplementation:SequenceDiagramRecommendation}). This request contains preferences and the current configuration. \texttt{M.Recommend} generates a recommendation based on the received data as a response. The recommended finished configuration is now sent from  \texttt{M.Collab} via broadcast over WebSockets to all M.Customer clients. 
+
+\begin{figure}[b]
+    \centering
+    \includegraphics[width=0.95\textwidth]{./figures/50_design_and_implementation/sequence_diagram_generating_recommendation.pdf}
+    \caption[Sequence Diagram: Recommendation Generation]{A sequence diagram showing the recommendation process.}
+    \label{fig:DesignImplementation:SequenceDiagramRecommendation}
+\end{figure}
+
+The REST API defines the following Endpoints and uses \emph{JSON} \cite{IntroducingJSON}.
+
+\begin{description}
+    \item[/recommender/] is reachable via a POST request and accepts a configuration state and preferences. Based on that a finished configuration is sent back as response.
+    \item[/config/] accepts GET and POST requests. Sending a GET request results in a list of all stored configurations being sent back. POST is used for adding a finished configuration to the configuration database.
+    \item[/product\_structure/] is reachable via GET and PUT. GET returns the current product structure and PUT is there to replace it.
+\end{description}
+
+The API is implemented with a minimal amount of functions and the recommender currently only supports one product at a time. However, the architecture is built in a way that it can be easily extended into supporting multiple products.
+The general architecture adheres to a \emph{model-view-controller} \cite{gamma2015design} inspired architecture.
+Data is stored and retrieved using \emph{data access objects}. Therefore, the currently used simple TinyDB database backend can be replaced with a different one easily.
+
+\section{Database}
+\label{sec:DesignImplementation:Database}
+
+The choice among database systems has to be made between \emph{non-relational} and \emph{relational} databases. Relational databases are good in regard to at the four ACID (atomicity, consistency, isolation, durability) principles \cite{chrysanthis1998recovery, cookACIDBASEDatabase2009}. Moreover, if the data structures are not changing it provides a solid basis that keeps the data reliable. A non-relational database on the other hand is ideal for rapid agile development. Besides, it excels if data requirements are not entirely clear and if a large amount of unstructured  data has to be stored. Furthermore, non-relational databases allow the system to store the data in any kind of structure. This proves an advantage as it allows to use the same data structure to be stored that also has to be fed out through the api. Hence, parsing methods for the API can be reused and altered upon changing requirements. Moreover, the data used for the recommender is mostly not interconnected. Therefore a relational databases' main strength, the data structure, does not really come into play here. Thus, in this thesis a non-relational database is used. 
+
+\section{Scoring Functions}
+\label{sec:DesignImplementation:ScroingFunctions}
+
+\begin{figure}[tb]
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/50_design_and_implementation/class_diagram_scoring_functions_simplified.pdf}
+    \caption[Class Diagram: Scoring Functions]{A simplified class diagram of the classes and interface related to scoring functions. }
+    \label{fig:DesignImplementation:ClassDiagramScoringFunctions}
+\end{figure}
+
+This section describes the implementation of scoring functions (see \autoref{fig:DesignImplementation:ClassDiagramScoringFunctions}) which are implemented in a modular fashion. There are different types of scoring functions. Some are meant for evaluating user preferences (and inherit from the class \texttt{PreferenceScoringFunction}), others are meant for penalising changes in the current configuration state and therefore inherit from  \texttt{ConfigurationPenalty}. The last type of scoring function is \texttt{ReduceScoringFunction} that consists of one or multiple scoring functions and combines their scores to one. It uses the \emph{composite design pattern} \cite{gamma2015design}. These functions allow ´the flexible combination of functions and, in addition due to their underlying structure they can use similar components. For example, preference scoring functions use a \emph{pipeline} \cite{gamma2015design} to first convert preferences to a list of values. This is done using \texttt{PreferenceToListFunction}. A list can be modified with a \texttt{ListToListFunction}. This step is optional. The last mandatory step is to convert the list to a value using \texttt{ListToValueFunction}. After this step, the value can be modified further, for example with a threshold function or any other function. All these components allow the easy and quick creation of new scoring functions. A factory (\texttt{ScoringFunctionFactory}) is used to assemble a scoring function based on input parameters. This provides flexibility for quickly implementing new types of scoring functions.
+
+\section{Technologies}
+\label{sec:DesignImplementation:Technologies}
+
+\texttt{M.Recommend} is built in \emph{Python} \cite{PythonOrg} using \emph{FlaskRESTPlus} \cite{FlaskRESTPlus13Documentation}. \emph{FlaskRESTPlus} is an extension of \emph{Flask} \cite{FlaskDocumentation} that is meant to build a REST-Service and allows the generation of a web UI which documents the API. \emph{Flask} is a WSGI \cite{WhatWSGI} framework that is meant to easily get started with development while maintaining the ability to scale up to complex applications.
+
+As Database system \emph{TinyDB} \cite{TinyDB15Documentation} with the extension \emph{tinyrecord} \cite{junEugeneeeoTinyrecord2020} is being employed. \emph{TinyDB} is a simple well tested NoSQL database framework that can store documents that are represented as dictionaries. The extension \emph{tinyrecord} adds atomic transaction support with a record-first then execute architecture.
+
+As testing library for unit tests \emph{pytest} \cite{PytestDocumentation} is being employed which is commonly used for python. It supports automatic test discovery.
+
+For the evaluation of data and also for some operations with lists \emph{numpy} \cite{NumPy} is being employed. This library is optimised for fast scientific computing. 
+
+Visualisation is done using \emph{Matplotlib} \cite{MatplotlibDocumentation} in combination with \emph{pandas} \cite{PandasPythonData}. \emph{Pandas} is a powerful framework for data analysis and data manipulation which in the context of this thesis is being employed to load evaluation data.
+
+
+\section{Requirements Assessment}
+\label{sec:DesignImplementation:RequirementsAssesment}
+
+The requirements posed in \autoref{sec:Concept:Requirements} are assessed in this section. The recommender uses a continuous value range between zero and one for preferences. Additionally, the recommendation component is designed to be used independently, therefore it is possible to use it without proprietary software.
+Additionally, the group scoring function takes into account preferences of each group member and the current configuration state. The recommendation system can be used by multiple users at the same time as all information needed for a recommendation is sent with the recommendation request. Accordingly, multiple group configurators can use the system at the same time. Furthermore, only valid solutions are recommended because the recommender system's configuration database stores finished configurations. Moreover, the system responds in a timely manner with all components running on the same machine, a modern laptop.
+Consequently, all mandatory requirements have been fulfilled.
+
+The user interface is simple because a user only has two possible opportunities for action. They can change their preferences with a slider or change the configuration state. Additionally, the system is made to be compatible with other configuration systems. These have to implement the correct API-calls. However, this requirement was not tested with a different configuration system. Different scoring functions can be used however they can neither be selected through user interface nor through the API. Thus, this requirement is only partially fulfilled but the system allows extension to fulfil this requirement fully.
+Overall optional requirements have been addressed but they have not been thoroughly evaluated and some of them have only been partially implemented.

30_Thesis/sections/60_evaluation.tex

@@ -0,0 +1,319 @@
+\chapter{Evaluation}
+\label{ch:Evaluation}
+
+In this chapter the prototype is evaluated in terms of its functionality and its properties. The evaluation is an offline evaluation with synthetic data. All possible valid configurations are generated for one use case, i.e. all possible valid configurations for the forestry use case. Moreover, groups with explicit preferences and a configuration state (which, e.g. would be the currently existing forest) are generated, too. The evaluation will show that the recommender produces good results overall.
+
+\section{Metric}
+\label{sec:Evaluation:Metrics}
+
+A metric is required to carry out the validation. The proposed metric is the metric of satisfaction. This metric was created because pertinent literature does not provide metrics usable for this thesis. Satisfaction is quantified in this thesis by a threshold metric. A user's preference is used to calculate a rating for each possible solution. Each configuration solution gets an individual score determined by the user's preferences. The score is calculated using the average of a user's preference for each characteristic that is part of the configuration. The result allows a configuration to be compared to all other configurations and ranked according to the percentage of configurations that it beats for a specific user. The threshold metric consists of two parameters. First the threshold center $tc$ and second the satisfaction distance $sd$. The threshold for a satisfied person is at $tc + sd$ and for a dissatisfied person is at $tc - sd$. If a recommendation lies in between these two thresholds the person is classified to neither be satisfied nor be dissatisfied with the solution. For this thesis $sd=5\%$ will be used. This choice is based on the assumption that people switch from satisfied to dissatisfied rather quickly. Therefore, the parameter considered in this thesis is $tc$. An example is the choice of $tc = 60\%$. This results in a person satisfied with a recommendation if it is better than at least $65\%$ of all possible finished configurations. In contrast, a person is dissatisfied if the recommendation is not better than $55\%$ of all possible finished configurations. A recommendation that is better than at least $55\%$ and not better than $65\%$ of all possible solutions is considered neutral by the individual.
+
+Different $tc$ values allow to model different situations. A situation with a low willingness to compromise is modelled by a high $tc$. A contrary situation with a group that has a high willingness to compromise is modelled by a low $tc$.
+
+A satisfaction and dissatisfaction classification allows groups to be measured by the amount of people that are satisfied and dissatisfied. Moreover, changes in satisfaction and dissatisfaction for different parameters can be compared. A reasonable $tc$ value has to be found for groups otherwise any derived metrics will not show any meaningful results.
+
+\section{Evaluation Objective}
+\label{sec:Evaluation:Questions}
+
+This section poses three questions that will be answered during the evaluation. The questions' aim is to guide through this chapter. They set the guidelines for this evaluation and define its focuses. The questions answered during the evaluation are:
+
+\begin{itemize}
+    \item Main question: How does the satisfaction with a group decision, guided by the recommender, differ from the decision of a single decision maker, the dictator, who does not take the other group members' opinions into account?
+    \item How many group members on average are satisfied with the group decision?
+    \item How does the amount of stored finished configurations relate to satisfaction with a recommendation?
+\end{itemize}
+
+The main question is addressed to understand the behaviour the recommender and whether it gives benefits to groups. The second question is aimed at providing information regarding the data and what satisfaction looks like in group decisions and which factors influence it. Last, a technical question is posed. This question is relevant because it shows technical aspects of the recommender. This is important since other work for using the recommender in other possibly larger use cases depend on performance figures in relation to number of stored configurations.
+
+
+\begin{table}[tb]
+    \tiny
+    \begin{center}
+        \setlength\tabcolsep{3pt}
+        \begin{tabularx}{\columnwidth}{cl|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|C|}
+            & & \multicolumn{3}{c|}{\textit{indigenous}} & \multicolumn{3}{c|}{\textit{resilient}} & \multicolumn{3}{c|}{\textit{usable}} & \multicolumn{3}{c|}{\textit{effort}} & \multicolumn{3}{c|}{\textit{quantity}} & \multicolumn{3}{c|}{\textit{price}} & \multicolumn{3}{c|}{\textit{accessibility}} \\
+            & & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{manual} & \rotatebox[origin=c]{90}{harvester} & \rotatebox[origin=c]{90}{\ autonomous} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} & \rotatebox[origin=c]{90}{low} & \rotatebox[origin=c]{90}{moderate} & \rotatebox[origin=c]{90}{high} \\
+
+            \hline
+            \multirow{3}{*}{\textit{indigenous}}    & low       & - & - & - &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   \\ \cline{2-23}
+                                                    & moderate  & - & - & - &   &   & n &   &   & n &   &   &   &   &   &   &   &   &   &   &   &   \\ \cline{2-23}
+                                                    & high      & - & - & - &   &   & n &   & n & n &   &   &   &   &   & n & n &   &   &   &   &   \\ \hline
+
+            \multirow{3}{*}{\textit{resilient}}     & low       &   &   &   & - & - & - &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   \\ \cline{2-23}
+                                                    & moderate  &   &   &   & - & - & - &   &   & n &   &   &   &   &   &   &   &   &   &   &   &   \\ \cline{2-23}
+                                                    & high      &   & n & n & - & - & - &   & n & n &   &   &   &   &   & n & n & n &   &   &   &   \\ \hline
+
+            \multirow{3}{*}{\textit{usable}}        & low       &   &   &   &   &   &   & - & - & - &   &   &   &   &   & n & n & n &   &   &   &   \\ \cline{2-23}
+                                                    & moderate  &   &   & n &   &   & n & - & - & - &   &   &   &   &   &   &   &   &   &   &   &   \\ \cline{2-23}
+                                                    & high      &   & n & n &   & n & n & - & - & - &   &   &   &   &   &   &   &   &   &   &   & n \\ \hline
+
+            \multirow{3}{*}{\textit{effort}}        & low       &   &   &   &   &   &   &   &   &   & - & - & - &   &   & n & n & n &   &   &   &   \\ \cline{2-23}
+                                                    & moderate  &   &   &   &   &   &   &   &   &   & - & - & - &   &   &   &   &   &   &   & n & n \\ \cline{2-23}
+                                                    & high      &   &   &   &   &   &   &   &   &   & - & - & - &   &   &   &   &   &   &   & n & n \\ \hline
+
+            \multirow{3}{*}{\textit{quantity}}      & low       &   &   &   &   &   &   &   &   &   &   &   &   & - & - & - & n & n &   &   &   &   \\ \cline{2-23}
+                                                    & moderate  &   &   &   &   &   &   &   &   &   &   &   &   & - & - & - & n & n &   &   &   &   \\ \cline{2-23}
+                                                    & high      &   &   & n &   &   & n & n &   &   & n &   &   & - & - & - &   &   &   &   & n & n  \\ \hline
+
+            \multirow{3}{*}{\textit{price}}         & low       &   &   & n &   &   & n & n &   &   & n &   &   & n & n &   & - & - & - &   &   &   \\ \cline{2-23}
+                                                    & moderate  &   &   &   &   &   & n & n &   &   & n &   &   & n & n &   & - & - & - &   &   &   \\ \cline{2-23}
+                                                    & high      &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   & - & - & - &   &   &   \\ \hline
+
+            \multirow{3}{*}{\textit{accessibility}} & low       &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   &   & - & - & - \\ \cline{2-23}
+                                                    & moderate  &   &   &   &   &   &   &   &   &   &   & n & n &   &   & n &   &   &   & - & - & - \\ \cline{2-23}
+                                                    & high      &   &   &   &   &   &   &   &   & n &   & n & n &   &   & n &   &   &   & - & - & - \\ \hline
+        
+        \end{tabularx}
+        \caption[Forestry Use Case: Constraints]{Constraints in \emph{not with} form for the forestry use case.}
+        \label{tab:Evaluation:UseCase}
+    \end{center}
+\end{table}
+
+\section{Use Case}
+\label{sec:Evaluation:UseCase}
+The evaluation uses the forestry use case. This is a use case with four stakeholders. \autoref{fig:Concept:ForestExample} presents the attributes and characteristics of this use case. \autoref{tab:Evaluation:UseCase} extends it with rules that dictate which configurations valid.
+
+The stakeholders in this use case are: a forest owner, an athlete, an environmentalist, and a consumer. The owner sees the forest as an investment, they are interested in a high long-term profit. On the other hand consumers are interested in reasonable wood price as they use wood for furniture and also for their fireplaces. In contrast, the environmentalist is interested in a healthy forest that is not impacted negatively by human activity. Last is the athlete who is interested in good accessibility of the forest and that there is some plant and animal life.
+Every group consists of four people which is why they need to try and find a compromise. Diverging preferences make this difficult. All stakeholders have an interest in getting their will but also all parties need others to accept the decision. It is not in the interest of a stakeholder to fully have their preferences met while ending up with protests that arise from the deep dissatisfaction of other group members.
+
+
+
+\section{Data Generation}
+\label{sec:Evaluation:GeneratingGroups}
+
+This section describes the data generation process as seen in \autoref{fig:Evaluation:GeneratingDataProcess} that generates data based on the use case in \autoref{sec:Evaluation:UseCase}. Group profiles are used to generate groups of four with different group member types. The exact group composition depends on the group type. For every parameter and group type $1000$ groups are generated and converted to preferences. This number was chosen as it is the highest number that allows computing times to work overnight on the hardware that is available. Also this number is large enough to reduce strong variability between runs. For each group unfinished configurations are generated and its preferences are paired up with the generated unfinished configurations. These pairs later on are used for the evaluation. 
+
+\begin{figure}[htb]
+    \centering
+    \includegraphics[width=0.9\textwidth]{./figures/60_evaluation/bpmn_evaluation_input_data_generation.pdf}
+    \caption[Data Generation Process]{Data generation process for the evaluation}
+    \label{fig:Evaluation:GeneratingDataProcess}
+\end{figure}
+
+\subsection{Unfinished Configurations Generation}
+
+Unfinished configurations are generated using all finished configurations and taking a subset of the contained characteristics. This way all generated configurations will be valid and lead to valid solutions. For the results that are presented in this chapter around $\frac{1}{7} \approx 15\%$ of characteristics is maintained. This value is chosen to allow the existing configuration to take effect but not to skew the results due to the penalty function severely limiting possible options.
+
+\subsection{Preference Generation}
+
+For the forestry use case, the idea is that there are multiple types of user profiles. Each group profile is represented by a neutral, negative or positive attitude towards a characteristic. During data generation the attitude is converted to a preference using a normal distribution. Each preference lies in the interval $[0,1]$. Zero can be seen as worst possible option and one as best possible option. \autoref{fig:Evaluation:PreferenceDistribution} shows how the user profile can be converted to preferences. The actual group member profiles are shown in \autoref{tab:Evaluation:GroupMemberMappings}.
+
+\pgfplotsset{height=5cm,width=\textwidth,compat=1.8}
+\pgfmathdeclarefunction{gauss}{2}{%
+  \pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}%
+}
+\begin{figure}[tb]
+    \begin{tikzpicture}
+        \begin{axis}[
+            every axis plot post/.append style={
+                mark=none, domain=0:1, samples=50, smooth
+            },
+            axis x line*=bottom,
+            xmin=0,
+            xmax=1,
+            ymin=0.1,
+            xticklabel style={
+                /pgf/number format/precision=3,
+            },
+            xtick={0,0.25, 0.5, 0.75,1},
+            hide y axis]
+          \addplot [draw=black, style={dashdotdotted}][very thick] {gauss(0.25,0.1)} node[text=black][above,pos=0.5] {negative};
+          \addplot [draw=black, style={solid}][very thick] {gauss(0.5,0.05)} node[text=black][above,pos=0.48] {neutral};
+          \addplot [draw=black, style={dotted}][very thick] {gauss(0.75,0.1)} node[text=black][above,pos=0.5] {positive};
+        \end{axis}
+        \end{tikzpicture}
+ \caption[Preference Distribution]{Distribution of preferences for a user type.}
+\label{fig:Evaluation:PreferenceDistribution}
+\end{figure}
+
+
+\begin{table}[htb]
+    \begin{center}
+        \begin{tabular}{l|c|c|c|c}
+            characteristic                              & athlete           & forest owner      & environmentalist  & consumer          \\
+            \hline
+            $(\textit{indigenous}, \text{low})$         & \textbf{negative} & \textit{positive} & \textbf{negative} & neutral           \\
+            $(\textit{indigenous}, \text{moderate})$    & \textit{positive} & neutral           & \textbf{negative} & neutral           \\
+            $(\textit{indigenous}, \text{high})$        & \textit{positive} & \textbf{negative} & \textit{positive} & \textbf{negative} \\
+            \hline
+            $(\textit{resilient}, \text{low})$          & neutral           & \textit{positive} & neutral           & neutral           \\
+            $(\textit{resilient}, \text{moderate})$     & \textit{positive} & neutral           & neutral           & neutral           \\
+            $(\textit{resilient}, \text{high})$         & \textit{positive} & \textbf{negative} & \textbf{negative} & \textbf{negative} \\
+            \hline
+            $(\textit{usable}, \text{low})$             & neutral           & neutral           & neutral           & \textbf{negative} \\
+            $(\textit{usable}, \text{moderate})$        & neutral           & neutral           & \textbf{negative} & neutral           \\
+            $(\textit{usable}, \text{high})$            & \textbf{negative} & \textit{positive} & \textbf{negative} & \textit{positive} \\
+            \hline
+            $(\textit{effort}, \text{manual})$          & \textbf{negative} & neutral           & \textit{positive} & \textbf{negative} \\
+            $(\textit{effort}, \text{harvester})$       & \textbf{negative} & \textit{positive} & \textbf{negative} & neutral           \\
+            $(\textit{effort}, \text{autonomous})$      & \textbf{negative} & \textit{positive} & \textbf{negative} & neutral           \\
+            \hline
+            $(\textit{quantity}, \text{low})$           & \textit{positive} & \textbf{negative} & \textit{positive} & \textbf{negative} \\
+            $(\textit{quantity}, \text{moderate})$      & neutral           & \textit{positive} & neutral           & \textbf{negative} \\
+            $(\textit{quantity}, \text{high})$          & \textbf{negative} & \textit{positive} & \textbf{negative} & \textit{positive} \\
+            \hline
+            $(\textit{price}, \text{low})$              & neutral           & neutral           & neutral           & \textit{positive} \\
+            $(\textit{price}, \text{moderate})$         & neutral           & \textit{positive} & neutral           & neutral           \\
+            $(\textit{price}, \text{high})$             & neutral           & \textit{positive} & neutral           & \textbf{negative} \\
+            \hline
+            $(\textit{accessibility}, \text{low})$      & \textbf{negative} & \textit{positive} & \textit{positive} & neutral           \\
+            $(\textit{accessibility}, \text{moderate})$ & neutral           & neutral           & neutral           & neutral           \\
+            $(\textit{accessibility}, \text{high})$     & \textit{positive} & \textbf{negative} & \textbf{negative} & neutral           \\
+            \hline
+        \end{tabular}
+        \caption[Forestry Use Case: Group Member Profiles]{The attitudes of each group member profile.}
+        \label{tab:Evaluation:GroupMemberMappings}
+    \end{center}
+\end{table}
+
+These user profiles can be used to generate rather homogenous groups but also to create groups that have interests that are more conflicting. The following group types, with four members each, are generated:
+
+\begin{itemize}
+    \item random groups (preferences are uniformly random)
+    \item heterogeneous groups (people adhere to one preference profile like forest owner, athlete, consumer, environmentalist)
+    \item homogeneous groups (only one preference profile for all group members which in this evaluation is the forest owner)
+\end{itemize}
+
+The natural group type for the use case is a heterogeneous group but to widen the evaluation and to see how the recommender performs with different types of groups the two other group types are evaluated too. As a result, more general statements about the recommender's performance can be made.
+
+\subsection{The Effect of Stored Finished Configurations}
+
+Another important component of the evaluation is the influence of stored finished configurations. When evaluating a subset of stored finished configurations it is important to avoid outliers. This is the reason why a process inspired by \emph{cross validation} \cite{kohaviStudyCrossValidationBootstrap1995} is used. The configuration database is randomly ordered and sliced into sub-databases of the needed size. As an example, if the evaluated stored data size is 20, a configuration database containing 100 configurations is split into five sub-databases of size 20. Now the evaluation is carried out for each of the sub-databases and finally the average is determined. This avoids the random picking of a subset which either performs much better than most other possible combinations of databases or which performs much worse. This way the data is more aligned to the expected value.
+
+\section{Hypotheses}
+\label{sec:Evaluation:Hypotheses}
+
+This section gives an overview on the hypotheses tested during data analysis. Each hypothesis is followed by an explanation as to why the hypothesis is presented. In later sections the truthfulness of the hypothesis is examined. This allows to verify if expectations about the behaviour of the recommender are true or false.
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:MaximumMinimum} Improvements for group recommendations are highest when the number of people satisfied with the dictator's decision is slightly lower than two and the highest reduction in dissatisfied group members can be seen at around two group members dissatisfied respectively.
+    \end{itshape} \medskip \\*    
+    This is based on the assumption that in a real situation a group of four with less than two satisfied members (with a dictator's decision) on average, has enough room for improvement so that potentially three group members can be satisfied after the use of the recommender. This means that at least one more person is satisfied with the compromise. In some groups it my then be potentially possible to shift the last person from dissatisfaction towards a neutral attitude. A higher base satisfaction is assumed to reduce the possibility to make an additional group member satisfied.
+\end{hypothesis}
+
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:HigherTcLessSatisfied} A higher $tc$ value results in less satisfied people and more dissatisfied people with regard to the dictator's decision.
+    \end{itshape} \medskip \\*
+    A higher $tc$ value causes a person to be dissatisfied with a higher amount of configurations. It also causes a person to be satisfied with less configurations. Therefore, recommending a random configuration causes the chance of making an individual satisfied to sink while increasing the chance of that person to be dissatisfied. Already the change in probability leads to the assumption that this result should be seen with non-random recommendations too.
+\end{hypothesis}
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:OnlyOneSatisfied} There exists a $tc$ value which causes only one person to be classified as satisfied with the dictator's decision and no one is classified as satisfied with the group recommender's decision.
+    \end{itshape} \medskip \\*
+    A $tc$ value that reaches a high enough level eventually should make only the dictator herself satisfied with the dictator's decision. The bound for satisfaction is so high that any group recommendation will cause the dictator to also be dissatisfied or at least neutral with the group decision. This can be understood as a complete unwillingness of a group to compromise. All group members are only satisfied with their own decision. Having two group members with identical interest, which is expected to be rare, results in this effect not being present even in a situation like that. 
+\end{hypothesis}
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:HomogenousMoreSatisfied} Homogeneous groups have more members satisfied with the recommender's decision but also with the dictator's decision compared to heterogeneous groups.
+    \end{itshape} \medskip \\*
+    As the interest in homogenous groups is more aligned it is to be expected that the overall satisfaction levels for more homogenous groups is higher. If the base level is already higher it is likely that even just a slight increase shifts recommendations for homogenous groups to satisfaction levels not achievable by heterogeneous groups.
+\end{hypothesis}
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:HeterogenousBiggerSatisfactionIncrease} More heterogeneous groups see a bigger increase in satisfaction than less heterogeneous groups when switching from a decision of a dictator to a decision made by the recommender.
+    \end{itshape} \medskip \\*
+    The assumption is made that in more heterogeneous groups the satisfaction with the dictator's decision is less. Therefore, there is a higher possible increase in satisfaction. A homogenous group that already satisfies all group members with the dictator's decision cannot see an increase in satisfaction, therefore, the assumption is made that with a higher number of people dissatisfied or neutral with the dictator's decision, more people will be be lifted into satisfaction and the increase in satisfaction will be bigger. However a group that has divergent interests actually might not be able to reach high levels of satisfaction.
+\end{hypothesis}
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:StoreSizeBetterResults} A higher amount of stored finished configurations results in a higher number of satisfied and a lower number of dissatisfied group members when the recommender is used to make the group decision.
+    \end{itshape} \medskip \\*
+    This hypothesis is based on the fact that the possibility to chose a bigger pool of configurations increases the chances of arriving at a good recommendation. This of course requires the assumption that aggregation strategies that pick recommendations pick configurations that also fare better in the chosen satisfaction metric. If this is not the case this hypothesis is not sustainable.
+\end{hypothesis}
+
+\begin{hypothesis}
+    \begin{itshape}
+        \label{hyp:Evaluation:AggregationStrategies} The multiplication and best average aggregation strategies perform better than the least misery aggregation strategy.
+    \end{itshape} \medskip \\
+    Best average and multiplication are strategies that perform best in some of the by \citeauthor{Masthoff2015} \cite[~ 755f]{Masthoff2015} listed online experiments. Therefore, it is reasonable to assume that they perform well here too. Least misery was listed in some studies as performing worst. Accordingly, it is expected to fare less good than other group aggregation strategies.
+\end{hypothesis}
+
+\section{Results}
+\label{sec:Evaluation:Findings}
+
+\subsection{Threshold Center Selection}
+This section aims at finding a $tc$ parameter for the analysis. This is required to reduce the amount of data that has to be looked at and to get valuable results. For this purpose all parameters except $tc$ will be fixed. The preference aggregation strategy looked at is multiplication because this strategy shows good results across the board when briefly looking at the generated data. The configuration database is used with all possible solutions (which is 148 in total). This results in a bigger visible effect in terms of satisfaction and dissatisfaction change as the recommender has access to all possible configurations and also provides more solid and predictable results. \autoref{fig:Evaluation:tcChange} shows the satisfaction change based on choice of $tc$. Of note is that the maxima of satisfaction change precedes the minima of dissatisfaction change for all group types. Maxima and minima occur at different tc values depending on the group type. Heterogeneous groups peek earliest while homogenous groups only show a peek towards the maximum $tc$ value. Changes in dissatisfaction are minimal even with $tc$ close to its maximum value for homogeneous groups. 
+
+\autoref{fig:Evaluation:tcCount} shows the amount of group members satisfied and dissatisfied with the dictator's decision. The number of satisfied people decreases with an increasing $tc$ and its downward movement accelerates. The dissatisfaction curve shows a similar trend in reverse. Here the number of dissatisfied group members increases with an increase in $tc$. The curve accelerates its growth analogous to the acceleration of the satisfaction curve. The behaviour of heterogeneous groups and random groups is similar but the curve for heterogeneous groups shows less satisfaction and more dissatisfaction for a given tc. Also both curves have a negative satisfaction change when $tc$ reaches a certain height. Homogeneous groups only have satisfied group members for most $tc$ values but they decrease rapidly for values greater than $85$. Dissatisfied group members are at zero for the whole value range of $tc$ except a very slight upward tick at the end that is barely noticeable.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/60_evaluation/tc_change__multi__db-size-148.pdf}
+    \caption[Satisfaction and Dissatisfaction: Change based on $tc$]{The average satisfaction and dissatisfaction change based on $tc$ with a database size of 148 and multiplication as aggregation strategy.}
+    \label{fig:Evaluation:tcChange}
+\end{figure}
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/60_evaluation/tc_dictator__multi__db-size-148.pdf}
+    \caption[Satisfaction and Dissatisfaction: Average based on $tc$]{The average satisfaction and dissatisfaction with the dictator's decision based on $tc$.}
+    \label{fig:Evaluation:tcCount}
+\end{figure}
+
+
+\autoref{hyp:Evaluation:MaximumMinimum} states that the highest satisfaction change is expected at places where the overall satisfaction with the dictator's decision is close to two. However, the data shows a slightly different result. This hypothesis does not hold true. When looking at the data we see peeks in satisfaction change when values are equal to $2.81, 2.51$ and $3$ (heterogeneous, random, homogenous). Therefore, the expectation does not hold up. Most likely this happens because at lower satisfaction numbers with the dictator's decision the threshold for satisfaction is set too high which causes the group compromise to classify less group members as satisfied. Moreover, valleys for dissatisfaction change are also not at the expected value of \textit{two}. They are instead at $1.19, 1.49, 0.04$ (heterogeneous, random, homogenous). Here the valleys are lower than expected. However, the data from homogenous groups seems to be cut off. Therefore, a judgement for homogenous groups is difficult and with slightly less heterogeneous groups this graph should show bigger effects.
+
+
+
+The predicted trend that a higher $tc$ results in a lower satisfaction and a higher dissatisfaction with the dictator's decision, as predicted by \autoref{hyp:Evaluation:HigherTcLessSatisfied}, can be clearly seen in \autoref{fig:Evaluation:tcCount} and has already been described in this section. This means for the evaluation that the behaviour of the recommender is predictable and suggests that the used metrics are modelling behaviour expected in reality.
+
+\autoref{hyp:Evaluation:OnlyOneSatisfied} predicts that the satisfaction with the individual decision eventually reaches one and that no one is satisfied with the group recommender's decision. This means the satisfaction change should decrease to minus one. \autoref{fig:Evaluation:tcCount} shows a downward trend that comes close to one for heterogeneous and random groups. Therefore, the trend suggests that the hypothesis holds true with regard to heterogeneous and random groups but the drop for homogenous groups just reaches below $2.8$ suggesting that the hypothesis does not hold for homogenous groups. Also, satisfaction change in heterogeneous groups decreases close to minus one while this value is neither fully reached by random groups nor by homogenous groups. The hypothesis therefore holds true only for heterogeneous groups. A likely cause why it does not seem to hold true for random or homogenous groups is that the highest tc value still includes multiple configurations and a recommended configuration keeps some group members satisfied for some of the time. For random groups it may also be possible that a group member other than the dictator could be satisfied with the group decision.
+
+During a group decision it is better to make one less person dissatisfied than to make one more person satisfied. Therefore, this thesis uses $tc$ values that are closer to the minima of dissatisfaction change than to the maxima of satisfaction change. The minima for heterogeneous groups is at $tc = 70\%$, therefore, this is the chosen value for evaluation of the remaining hypotheses. This is needed because otherwise analysis would be infeasible due to a too large parameter space. For random groups the minima of dissatisfaction change can be found at $tc = 85\%$ which is the value used for all following analyses of random groups. For homogenous group-dissatisfaction change is decreasing until the highest possible value of $tc$. Because of that $tc = 94\%$ is used for analysis.
+
+\subsection{Recommender Performance Analysis}
+
+\begin{figure}[p]
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/60_evaluation/heterogeneous_combined__amount-1000__tc-70}
+    \caption[Satisfaction and Dissatisfaction: Heterogeneous Groups]{Satisfaction and dissatisfaction using the group recommender for heterogeneous groups with $tc = 70$.}
+    \label{fig:Evaluation:HeteroSatisfaction}
+\end{figure}
+
+\begin{figure}[p]
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/60_evaluation/random_combined__amount-1000__tc-85}
+    \caption[Satisfaction and Dissatisfaction: Random Groups]{Satisfaction and dissatisfaction using the group recommender for random groups with $tc = 85$.}
+    \label{fig:Evaluation:RandomSatisfaction}
+\end{figure}
+
+\begin{figure}[p]
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/60_evaluation/homogeneous_combined__amount-1000__tc-94}
+    \caption[Satisfaction and Dissatisfaction: Homogeneous Groups]{Satisfaction and dissatisfaction using the group recommender for homogeneous groups with $tc = 94$.}
+    \label{fig:Evaluation:HomoSatisfaction}
+\end{figure}
+
+This subsection fixes parameters of $tc$. It describes the satisfaction change and the total amount of satisfied people with the recommender's decision dependent on the amount of stored configurations.
+
+\autoref{fig:Evaluation:HeteroSatisfaction} shows the relationship between satisfaction and dissatisfaction and the number of stored configurations. The left y-axis shows the change in satisfaction compared to a decision made by a dictator. The right axis shows the average number of satisfied group members. The left figure shows numbers for satisfaction and the right for dissatisfaction. On the left, higher numbers are better and on the right, lower ones (with regards to change). There are three graphs each. One for multiplication, one for least misery and one for best average. The graphs for satisfaction are similar to a logarithmic curve. The increase in change of satisfaction slows with a higher number of stored configurations. The change in satisfaction is always above zero and a satisfaction increase of more than three quarters of the maximum can already be seen at around 25 stored configurations. Moreover, the curve for multiplication is greater than all other curves for all parameters. Least misery reaches the lowest amount of change across all values. The minimum number of satisfaction change is $0$ for least misery, and $0.1$ for best average and multiplications. The highest number is around $0.3$ for least misery, $0.4$ for best average and $0.5$ for multiplication
+When looking at dissatisfaction change the graphs are all in the negative number range. Multiplication reaches the lowest number and best average the highest. The gap between all three functions is less than that of satisfaction increase. And overall the curves are flatter indicating that the change with 25 stored configurations already reaches close to five sixth of the minimum value. The highest number of satisfaction change is $-0.4$ for all strategies while the lowest number is around $-0.57$ for least misery, $-0.53$ for best average and $-0.63$ for multiplication.
+
+The graphs for homogenous (\autoref{fig:Evaluation:HomoSatisfaction}) and random groups (\autoref{fig:Evaluation:RandomSatisfaction}) have a similar shape but their values and slopes vary. The satisfaction change for homogenous groups is mostly negative, starting at $-2$, and only reaches a positive level for more than $100$ stored configurations with a value of $0.04$. Multiplication and best average have higher values than least misery here too. Moreover the dissatisfaction change is always positive with a value range of $[0,1]$, except for a slight fall below zero after more than $75$ configurations are stored.
+Random groups as seen in \autoref{fig:Evaluation:RandomSatisfaction} mostly have a positive change in satisfaction. Here, values range from $-0.55$ to $0.27$ for least misery, from $-0.27$ and $-0.28$ to $0.74$ for best average and multiplication. The change is higher than the change for heterogeneous groups. Dissatisfaction also changes similarly to heterogeneous groups. Here the values for random groups reach a lower level. They range from $0$ to $-0.59$ for least misery. Multiplication and best average both have a minimum value around $-0.21$ and behave similarly. The range goes down to $-0.84$ for best average and $-0.86$ for multiplication.
+
+\autoref{fig:Evaluation:HeteroSatisfaction} also shows the average number of group members satisfied and dissatisfied with the recommender's decision. Satisfaction with the recommender's decision starts at $2.4$ and quickly reaches $2.65$ for least misery and $2.8$ for best average and multiplication. The highest value for multiplication is at $2.89$. Dissatisfaction likewise quickly plateaus. Here values for different recommenders are closer together. They start at $0.74$ (least misery) to $0.78$ (best average) and fall as low as $0.62$ for least misery, $0.66$ for best average and $0.56$ for multiplication.
+
+When looking at the total numbers as shown in \autoref{fig:Evaluation:HomoSatisfaction} the value range for homogenous groups is much larger but the overall shape stays the same. Here satisfaction numbers go from $0.55$ to $2.95$. \emph{Least misery} performs visibly worse than \emph{multiplication} and \emph{best average} reaches only $2.7$. Dissatisfaction values range from $1.21$ to $0.01$ and the values are not really visibly distinguishable, except in the range of $[25,50]$. Least misery seems to have the highest number of dissatisfied group members in this range.
+
+Random groups have less overall satisfaction with $tc = 85\%$ as seen in \autoref{fig:Evaluation:RandomSatisfaction} when looking at the total numbers. Satisfaction numbers start from $1.33$ (least misery), $1.61$ (best average) and $1.6$ (multiplication) and go up to $2.15$ for least misery and $2.62$ for best average and multiplication. The dissatisfaction numbers start at $1.5$ for least misery and $1.27$ for best average and multiplication and level off at $0.9$ (least misery), $0.65$ (best average) and $0.63$ (multiplication). There is a big difference visible between least misery and the other two aggregation functions.
+
+\subsection{Discussion}
+
+Having described the data the remaining hypotheses are discussed.
+\autoref{hyp:Evaluation:HomogenousMoreSatisfied} states that homogenous groups have more satisfied members with regards to the dictator's and the group recommender's decision. \autoref{fig:Evaluation:tcCount} shows that this holds true for the dictator's decision as for every instance satisfaction in homogeneous groups is higher than that of other groups. However, \autoref{fig:Evaluation:HeteroSatisfaction}, \autoref{fig:Evaluation:HomoSatisfaction} and \autoref{fig:Evaluation:RandomSatisfaction} show that for satisfaction with the recommender's decision this does not hold true when looking at $tc$ values where the recommender performs best for each segment. In these cases the homogenous group only reaches the highest amount of satisfaction when the recommender has access to all stored configurations. With a decreasing number of stored configurations both random groups and heterogeneous groups achieve a higher satisfaction. This is likely to happen due to the similarity between group members. A recommender with imperfect knowledge and a size-reduced, configuration database generates results that are not good enough and cannot compete with the dictator who always finds the perfect individual match that group members of homogeneous groups are satisfied with. It is important to note that homogeneous groups show a higher number of satisfied people across the board when the same $tc$ values are used.
+
+\autoref{hyp:Evaluation:HeterogenousBiggerSatisfactionIncrease} states that the increase in satisfaction should be bigger for more heterogeneous groups. However, \autoref{fig:Evaluation:HeteroSatisfaction}, \autoref{fig:Evaluation:HomoSatisfaction} and \autoref{fig:Evaluation:RandomSatisfaction} show this not to be true. The recommendations for heterogeneous groups indeed cause a larger change in satisfaction compared to homogeneous groups but random groups cause a positive change of higher magnitude. Also, the decrease in dissatisfaction is higher among random groups. This possibly happens because random groups have more aligned interests and preferences among group members and, therefore, they do not diverge as much which results in compromises for the group that can satisfy more individual members. Likewise, the group preferences are still far enough apart to cause dissatisfaction and neutrality with the dictator's decision.
+
+The data shows that a larger configuration database causes the amount of satisfied group members to be greater than recommendations using a smaller database. With dissatisfaction the same is seen in the inverse. A larger configuration database causes the number of dissatisfied group members to drop compared to a small database. However, in some runs there have been instances of least misery that show a slight drop. This can be seen in \autoref{fig:Evaluation:HeteroSatisfaction} when comparing $74$ and $148$ as the number of stored configurations. Why this happens is not entirely clear but a cause might be that least misery just takes into account the worst performing group member of the group. Thus, it is possible that there is a second slightly worse solution, when comparing least misery scores, which actually has a slight advantage in terms of dissatisfaction. This second best configuration can cause it to be in the second database partition hence resulting in less dissatisfaction on average. \autoref{hyp:Evaluation:StoreSizeBetterResults} thus is supported by the data but it does not fully hold true when looking at least misery.
+
+\autoref{hyp:Evaluation:AggregationStrategies} states least misery performs worse than multiplication. For a change in satisfaction this can be seen across the board, however, for the change in dissatisfaction this is not true everywhere. \autoref{fig:Evaluation:HeteroSatisfaction} shows that least misery performs better than best average in terms of dissatisfaction reduction. This behaviour possibly occurs because an average metric yields the same results for heavily polarised decisions and decisions that everyone feels neutral about. Least misery on the other hand takes into account only the group member least satisfied with the decision and, therefore, this metric performs better. However, in other cases it performs visibly worse. Also notable is that multiplication performs best across the board. This supports the findings by \citeauthor{Masthoff2015} \cite[~p. 755f]{Masthoff2015} and further shows that the satisfaction model does show some similar results to online evaluations.
+
+To go back to the evaluation questions posed in \autoref{sec:Evaluation:Questions} this section has shown that for random and heterogeneous groups the recommender performs better than a dictator. The average satisfaction depends on the chosen parameters but for the chosen value range average satisfaction with the recommender decision lies above two and can reach close to three satisfied group members for a high number of stored configurations and for some group types. The amount of stored finished configurations plays an important role in the recommender's performance but with a fraction of stored configurations the recommender still yields good results. This shows that the recommender provides useful decision support for helping in group decisions. It provides a solid basis for groups and can help their group decision. Most decisions the recommender does improve group satisfaction which shows that the recommender is able to be used to improve group decisions.

30_Thesis/sections/80_conclusion.tex

@@ -0,0 +1,30 @@
+\chapter{Conclusion}
+\label{ch:Conclusion}
+
+This chapter summarises the thesis and describes the exact steps that were undertaken, starting with an overview of the foundations regarding group recommenders and configuration. Furthermore, a concept for a recommender for group-based configuration is introduced, implemented as prototype and evaluated. Last, limitations of this thesis and the recommender system are presented and further research is proposed.
+
+\section{Summary}
+\label{sec:Conclusion:Summary}
+
+This thesis was motivated by the research area of group-based configuration gaining more interest. As group decisions come with many problems and biases, recommender systems are used  to help with group decisions. This avoids mistakes and helps with reproducibility of successful group decisions. To date, no research has been carried out on recommendations regarding group-based configurations. This is why the research question of this thesis focuses on: "How can a group recommender translate individual preferences into recommendations that improve the overall satisfaction of group members while considering constraints given by the configuration state?". This thesis answers the research questions by proposing a concept, implementing it as a prototype and evaluating it. Thereby the viability of such a system and such an approach is shown.
+First, the thesis introduces foundations of product configuration and extends them to group-based product configuration. Next, recommender systems are introduced and three basic approaches, collaborative filtering, content-based filtering and constraint based recommendation, are compared. Last, the \hyperref[ch:Foundations]{foundations chapter} allots an introduction into group recommendation.
+Following\autoref{ch:Foundations} (Foundations), a concept for an item-based recommender for group-based configuration, is introduced. This concept uses a database of finished configurations to choose a configuration that fits the group best. Each user's preferences are used to assign a score to a configuration. The score of all group members is then aggregated into a group score and the best configuration from the database is recommended. The recommender also all penalties for deviating from the current configuration state.
+Later, the concept is implemented as an open source microservice which is integrated into an already existing group-based configurator. The source code is available at \cite{kuchelmeister13hannes11BachelorThesis}.
+Last, an offline metric for satisfaction is introduced and it is used for evaluation. Three group types are evaluated, homogenous groups, random groups and heterogeneous groups. Overall, the evaluation shows that the recommender yields good results for groups and helps groups to form a compromise. Satisfaction among group members increases overall. A simple item-based approach therefore  already improves group decisions by finding good compromises. This is also the case when the knowledge of the recommender is limited.
+
+\section{Limitations and Further Research}
+\label{sec:Conclusion:LimitationsFurtherResearch}
+
+Due to the scope of this thesis it was not feasible to analyse all possible scenarios for the proposed approach.
+
+An offline satisfaction metric is introduced in this thesis which may lead to results that differ from real life impressions of people. It has yet to be validated in a real-world setting. The validations of this metric allows the use of the introduced satisfaction metric for other scenarios that also lack suitable existing metrics. Moreover, understanding the relation between the introduced metric and actual satisfaction can help to form more accurate satisfaction models. This helps to understand how to find better compromises and, at the same time, such a metric can directly be used as a component of a group recommender.
+
+In this thesis only a single use case was examined. Therefore, different use cases could yield different results in terms of satisfaction and the tolerance of limited knowledge. It is unclear if different scenarios which are either more complex or have more features and characteristics yield the same results. The successful application to larger products could help with large complex projects that include multiple experts from differing areas that do not necessarily agree. Also, larger products potentially reach limitations of the recommender as the solution space grows quickly. An approach that clusters configurations and other means of optimization can help with performance. Complex and large products might lead to usability issues as users potentially are overwhelmed with choices and setting all preferences individually might be infeasible. Thus, indirect means of acquiring preferences should be incorporated for more complex and larger configurations. 
+
+Furthermore, groups were automatically generated and, thus, possibly a lack of resemblance to common real-world group constellations. Consequently, real world group constellations for decisions should be analysed. This will allow the validation of the implemented groups and can lead to a better understanding of generating synthetic groups that resemble actual groups. Thus, less time has to be spent in acquiring user data.
+
+Moreover, the recommender did not perform ideally with homogenous groups, especially when knowledge about the solution space was limited. Hence, methods of detecting homogenous groups could detect cases in which the recommender perform poorly and use other recommenders instead. A recommender that should be evaluated in this context is presented by \citeauthor{choudharyMulticriteriaGroupRecommender2020} \cite{choudharyMulticriteriaGroupRecommender2020}. This recommender fares better with homogeneous groups opposed to random groups.
+
+Additionally, the group size was fixed to four members and because \citeauthor{choudharyMulticriteriaGroupRecommender2020} note that their recommender does better in smaller groups, deviating results for differently sized groups are possible. Moreover, this approach can be extended to potentially allow a whole community of people to decide about neighbourhood changes. This could range from the layout of a new community centre, staffing, equipment and uses. Therefore, such approaches of group-based configuration can be used for public participation in projects helping communities to build trust and be more involved in decisions.
+
+Finally, the approach used in this thesis assumes a flat group hierarchy. Modelling knowledge and hierarchy of a group can help to improve group decisions further as supervisors do not feel overrun by their employees and knowledge of experts on certain parts of a product or solution can use that knowledge to guide the decision that area. Experts in other areas could have more say in areas of their expertise. Thus, decisions could be expert and hierarchy driven which should help with group satisfaction about compromises.

30_Thesis/sections/abstract_de.tex

@@ -0,0 +1,3 @@
+\Abstract
+
+Gruppenbasierte Konfiguration ist ein noch relativ junges Feld, das sich mit der Konfiguration von Produkten, Dienstleistungen und Lösungen durch Gruppen auseinandersetzt und Gruppenentscheidungen unterstützt. Gute Gruppenentscheidungen zu treffen ist jedoch komplex und nicht immer reproduzierbar. Empfehlungssysteme für Gruppen können hier Abhilfe schaffen. Diese wurden jedoch noch nicht für gruppenbasierte Konfiguration eingesetzt. In dieser Thesis wird ein Konzept zur Integration eines item-basierten Gruppenempfehlungssystems in einen Gruppen-Konfigurator vorgestellt, prototypisch implementiert und evaluiert. Das Konzept zeigt auf, wie Präferenzen einzelner Gruppenmitglieder kombiniert werden können, um eine Gruppenbewertung für eine Konfiguration zu erhalten. Dieser Ansatz wird genutzt, um die durch die Gruppe höchstbewertete Konfiguration aus einer Datenbank auszuwählen und zu empfehlen. Die Evaluation mit synthetischen Gruppen mit vier Personen erzielt gute Ergebnisse, insbesondere bei heterogenen Gruppen.

30_Thesis/sections/abstract_en.tex

@@ -0,0 +1,4 @@
+\Abstract
+
+Group-based configuration is a fairly new configuration approach that presents a scenario where a group of people is configuring a product, service, or solution. However, good group decisions are difficult to arrive at. Therefore, to facilitate group decisions, group recommender systems are used. Yet, they have not been applied to group-based configuration. This thesis proposes a concept for an item based recommender system that helps a group to find a consensus for a configuration decision. The concept shows how preferences of individual group members can be combined to give configurations a group score. The score is used to rank configurations in a database and recommend the configuration with the highest score.
+A prototype is implemented and evaluated with a newly introduced offline satisfaction metric and with synthetically generated groups. Overall the evaluation shows that the proposed concept works especially well for heterogeneous groups.

30_Thesis/sections/declaration.tex

@@ -0,0 +1,35 @@
+%% LaTeX2e class for student theses: Declaration of independent work
+%% sections/declaration.tex
+%% 
+%% Karlsruhe Institute of Technology
+%% Institute for Program Structures and Data Organization
+%% Chair for Software Design and Quality (SDQ)
+%%
+%% Dr.-Ing. Erik Burger
+%% burger@kit.edu
+%%
+%% Version 1.3.3, 2018-04-17
+
+\thispagestyle{empty}
+\null\vfill
+\noindent\hbox to \textwidth{\hrulefill} 
+\iflanguage{english}{I declare that I have developed and written the enclosed
+thesis completely by myself, and have not used sources or means without
+declaration in the text.}%
+{Ich versichere wahrheitsgemäß, die Arbeit
+selbstständig angefertigt, alle benutzten Hilfsmittel vollständig und genau
+angegeben und alles kenntlich gemacht zu haben, was aus Arbeiten anderer
+unverändert oder mit Änderungen entnommen wurde.}
+ 
+ 
+%% ---------------------------------------------
+%% | Replace PLACE and DATE with actual values |
+%% ---------------------------------------------
+
+\regressbydays{1} % change to actual signature date
+\textbf{Wiesbaden, \printfdate{british}}
+\vspace{1.5cm}
+ 
+\dotfill\hspace*{8.0cm}\\
+\hspace*{2cm}(\theauthor) 
+\cleardoublepage

30_Thesis/thesis.tex

@@ -0,0 +1,221 @@
+%% LaTeX2e class for student theses
+%% thesis.tex
+%% 
+%% Karlsruhe Institute of Technology
+%% Institute for Program Structures and Data Organization
+%% Chair for Software Design and Quality (SDQ)
+%%
+%% Dr.-Ing. Erik Burger
+%% burger@kit.edu
+%%
+%% See https://sdqweb.ipd.kit.edu/wiki/Dokumentvorlagen
+%%
+%% {$HeadURL: https://svnserver.informatik.kit.edu/i43/svn/lehre/Abschlussarbeiten-Vorlage/thesis.tex $}
+%% {$LastChangedDate: 2018-08-02 14:19:06 +0200 (Do, 02 Aug 2018) $}
+%% {$LastChangedRevision: 5705 $}
+%% {$LastChangedBy: burger $}
+
+%% Available page modes: oneside, twoside
+%% Available modes: draft, final (see README)
+\documentclass[twoside, english, final]{sdqthesis}
+
+%% ---------------------------------
+%% | Information about the thesis  |
+%% ---------------------------------
+
+%% Name of the author
+\author{Hannes F. Kuchelmeister}
+
+%% Title (and possibly subtitle) of the thesis
+\title{Decision Support for Group-Based Configuration using Recommender Systems}
+
+%% Type of the thesis 
+\thesistype{Bachelor's Thesis}
+
+%% Change the institute here, ``IPD'' is default
+% \myinstitute{Institute for \dots}
+
+%% You can put a logo in the ``logos'' directory and include it here
+%% instead of the SDQ logo
+% \grouplogo{myfile}
+
+%% Alternatively, you can disable the group logo
+\nogrouplogo
+
+%% The reviewers are the professors that grade your thesis
+\reviewerone{Prof. Dr. Ralf H. Reussner}
+\reviewertwo{Prof. Dr. Christof Weinhardt}
+
+%% The advisors are PhDs or Postdocs
+\advisorone{Dr. rer. nat. Robert Heinrich}
+%% The second advisor can be omitted
+\advisortwo{M.A. Jonas Fegert}
+
+%% Please enter the start end end time of your thesis
+\usepackage{texdate}
+\initdate{2019}{12}{16}
+
+\editingtime{
+    \printfdate{british}
+    \advancebydays{31}
+    \advancebymonths{3}
+}{ 
+        \regressbydays{1}
+        \advancebydays{27} % corona automatic extension (34 days) - 7 days earlier hand in
+        \printfdate{british}
+}
+
+\settitle
+
+%% --------------------------------
+%% | Settings for word separation |
+%% --------------------------------
+
+%% Describe separation hints here.
+%% For more details, see 
+%% http://en.wikibooks.org/wiki/LaTeX/Text_Formatting#Hyphenation
+\hyphenation{
+% me-ta-mo-del
+}
+
+%% --------------------------------
+%% | Ref Hypothesis                 |
+%% --------------------------------
+\newcommand{\hypothesisautorefname}{Hypothesis}
+
+%% --------------------
+%% |  Float Barriers  |
+%% --------------------
+\usepackage{placeins}
+
+%% --------------------
+%% |  Table Packages  |
+%% --------------------
+\usepackage{tabularx}
+\newcolumntype{C}{>{\centering\arraybackslash}X}
+\usepackage{multirow}
+
+%% --------------------
+%% |  Maths Packages  |
+%% --------------------
+\usepackage{amsmath}
+\usepackage{amssymb}
+
+%% --------------------
+%% | Plotting Packages |
+%% --------------------
+\usepackage{pgfplots}
+
+%% ---------------------
+%% | Generating Frames |
+%% ---------------------
+\usepackage[framemethod=TikZ]{mdframed}
+\newcounter{hypo}
+\mdtheorem[
+    nobreak=true,
+    linecolor=gray!60,
+    linewidth=1pt,
+    frametitlebackgroundcolor=gray!20,
+    frametitlefont=\sffamily\bfseries\color{black},
+]{hypothesis}{Hypothesis}
+
+%% --------------------------------
+%% | PDF Comments                 |
+%% --------------------------------
+\usepackage[colorinlistoftodos, obeyDraft]{todonotes}
+
+
+%% --------------------------------
+%% | Gantt Charts                 |
+%% --------------------------------
+\usepackage{pgfgantt}
+
+%% --------------------------------
+%% | Quotation                 |
+%% --------------------------------
+
+\usepackage{csquotes}
+\MakeOuterQuote{"}
+
+%% --------------------------------
+%% | Bibliography                 |
+%% --------------------------------
+
+%% Use biber instead of BibTeX, see README
+\usepackage[citestyle=numeric,style=numeric,sorting=none,backend=biber]{biblatex}
+\addbibresource{thesis.bib}
+
+%% --------------------------------
+%% | File Inputs                 |
+%% --------------------------------
+\usepackage{texosquery}
+
+%% ====================================
+%% ====================================
+%% ||                                ||
+%% || Beginning of the main document ||
+%% ||                                ||
+%% ====================================
+%% ====================================
+\begin{document}
+
+%% Set PDF metadata
+\setpdf
+
+%% Set the title
+\maketitle
+
+%% The Preamble begins here
+\frontmatter
+
+\input{sections/declaration.tex}
+
+
+\setcounter{page}{1}
+\pagenumbering{roman}
+
+%% ----------------
+%% |   Abstract   |
+%% ----------------
+ 
+%% For theses written in English, an abstract both in English
+%% and German is mandatory.
+%%
+%% For theses written in German, a German abstract is sufficient.
+%%
+%% The text is included from the following files:
+%% - sections/abstract
+
+\includeabstract
+
+%% ------------------------
+%% |   Table of Contents  |
+%% ------------------------
+\tableofcontents
+
+\listoffigures
+\listoftables
+
+%% -----------------
+%% |   Main part   |
+%% -----------------
+
+\mainmatter
+
+\input{sections/00_introduction.tex}
+\input{sections/10_foundations.tex}
+\input{sections/20_related_work.tex}
+\input{sections/40_concept.tex}
+\input{sections/50_design_and_implementation.tex}
+\input{sections/60_evaluation.tex}
+\input{sections/80_conclusion.tex}
+
+
+
+%% --------------------
+%% |   Bibliography   |
+%% --------------------
+
+%% Add entry to the table of contents for the bibliography
+\printbibliography[heading=bibintoc]
+\end{document}

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+            <Set Name="3-Fold" FirstPage="0" Rows="1" Columns="3">
+                <PageNames Name="Left Page"/>
+                <PageNames Name="Middle"/>
+                <PageNames Name="Right Page"/>
+            </Set>
+            <Set Name="4-Fold" FirstPage="0" Rows="1" Columns="4">
+                <PageNames Name="Left Page"/>
+                <PageNames Name="Middle Left"/>
+                <PageNames Name="Middle Right"/>
+                <PageNames Name="Right Page"/>
+            </Set>
+        </PageSets>
+        <Sections>
+            <Section Number="0" Name="0" From="0" To="0" Type="Type_1_2_3" Start="1" Reversed="0" Active="1"/>
+        </Sections>
+        <MASTERPAGE PAGEXPOS="100.001" PAGEYPOS="20.001" PAGEWIDTH="595.275590551181" PAGEHEIGHT="841.889763779528" BORDERLEFT="40" BORDERRIGHT="40" BORDERTOP="40" BORDERBOTTOM="40" NUM="0" NAM="Normal" MNAM="" Size="A4" Orientation="0" LEFT="0" PRESET="0" VerticalGuides="" HorizontalGuides="" AGhorizontalAutoGap="0" AGverticalAutoGap="0" AGhorizontalAutoCount="0" AGverticalAutoCount="0" AGhorizontalAutoRefer="0" AGverticalAutoRefer="0" AGSelection="0 0 0 0"/>
+        <PAGE PAGEXPOS="100.001" PAGEYPOS="20.001" PAGEWIDTH="595.275590551181" PAGEHEIGHT="841.889763779528" BORDERLEFT="40" BORDERRIGHT="40" BORDERTOP="40" BORDERBOTTOM="40" NUM="0" NAM="" MNAM="Normal" Size="A4" Orientation="0" LEFT="0" PRESET="0" VerticalGuides="" HorizontalGuides="" AGhorizontalAutoGap="0" AGverticalAutoGap="0" AGhorizontalAutoCount="0" AGverticalAutoCount="0" AGhorizontalAutoRefer="0" AGverticalAutoRefer="0" AGSelection="0 0 0 0"/>
+        <PAGEOBJECT OwnPage="0" PTYPE="6" XPOS="152.332953622181" YPOS="52.7880952315889" WIDTH="511.070834645669" HEIGHT="761.657" RADRECT="0" FRTYPE="3" CLIPEDIT="1" PWIDTH="0.992558509527654" PCOLOR="None" PCOLOR2="Black" COLUMNS="1" COLGAP="0" NAMEDLST="" SHADE="100" SHADE2="100" GRTYP="0" ROT="0" PLINEART="1" PLINEEND="0" PLINEJOIN="0" LOCALSCX="1" LOCALSCY="1" LOCALX="0" LOCALY="0" PICART="1" PLTSHOW="0" BASEOF="0" textPathType="0" textPathFlipped="0" FLIPPEDH="0" FLIPPEDV="0" SCALETYPE="1" RATIO="1" PRINTABLE="1" ANNOTATION="0" ANNAME=" path3019" TEXTFLOWMODE="0" TEXTFLOW="0" TEXTFLOW2="0" TEXTFLOW3="0" AUTOTEXT="0" EXTRA="0" TEXTRA="0" BEXTRA="0" REXTRA="0" FLOP="0" PFILE="" PFILE2="" PFILE3="" PRFILE="" EPROF="" IRENDER="1" EMBEDDED="1" LOCK="0" LOCKR="0" REVERS="0" TransValue="0" TransValueS="0" TransBlend="0" TransBlendS="0" isTableItem="0" TopLine="0" LeftLine="0" RightLine="0" BottomLine="0" isGroupControl="0" NUMDASH="0" DASHS="" DASHOFF="0" NUMPO="28" POCOOR="511.071 761.657 511.071 761.657 17.7923 761.657 17.7923 761.657 17.7923 761.657 7.91532 761.657 0 743.868 0 753.741 0 743.868 0 743.868 0 0 0 0 0 0 0 0 493.282 0 493.282 0 493.282 0 503.155 0 511.071 17.7923 511.071 7.91931 511.071 17.7923 511.071 17.7923 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 " NUMCO="28" COCOOR="511.071 761.657 511.071 761.657 -4.88487 761.657 -4.88487 761.657 -4.88487 761.657 -14.7618 761.657 -22.6772 743.868 -22.6772 753.741 -22.6772 743.868 -22.6772 743.868 -22.6772 0 -22.6772 0 -22.6772 0 -22.6772 0 493.282 0 493.282 0 493.282 0 503.155 0 511.071 17.7923 511.071 7.91931 511.071 17.7923 511.071 17.7923 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 511.071 761.657 " NUMGROUP="0" GROUPS="" startArrowIndex="0" endArrowIndex="0" OnMasterPage="" ImageClip="" ImageRes="1" Pagenumber="0" isInline="0" fillRule="0" doOverprint="0" gXpos="29.6547882678499" gYpos="32.787095231589" gWidth="595.275590551181" gHeight="841.891150885822" LAYER="0" BOOKMARK="0" NEXTITEM="-1" BACKITEM="-1">
+            <trail/>
+            <PageItemAttributes/>
+        </PAGEOBJECT>
+    </DOCUMENT>
+</SCRIBUSUTF8NEW>

Additional_Notes/MerlinCollabRecommender.drawio

@@ -1 +0,0 @@
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Additional_Notes/bpmn/configuration_process.bpmn

@@ -0,0 +1,207 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<bpmn:definitions xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" id="Definitions_0hcliey" targetNamespace="http://bpmn.io/schema/bpmn" exporter="bpmn-js (https://demo.bpmn.io)" exporterVersion="6.3.0">
+  <bpmn:process id="Process_0aaqeef" isExecutable="false">
+    <bpmn:exclusiveGateway id="Gateway_0rs0dwe" name="Have all preferences of all users been entered?">
+      <bpmn:incoming>Flow_0pmix2e</bpmn:incoming>
+      <bpmn:incoming>Flow_183a3gl</bpmn:incoming>
+      <bpmn:outgoing>Flow_13jervv</bpmn:outgoing>
+      <bpmn:outgoing>Flow_0sd24xk</bpmn:outgoing>
+    </bpmn:exclusiveGateway>
+    <bpmn:task id="Activity_1konb1u" name="generate recommendation">
+      <bpmn:incoming>Flow_0sd24xk</bpmn:incoming>
+      <bpmn:outgoing>SequenceFlow_14bjmv4</bpmn:outgoing>
+      <bpmn:property id="Property_03b25wa" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_0oxxzci">
+        <bpmn:sourceRef>Flow_0ug7unw</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataInputAssociation id="DataInputAssociation_0j2qf1y">
+        <bpmn:sourceRef>Flow_00hy6u9</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataInputAssociation id="DataInputAssociation_052ijwq">
+        <bpmn:sourceRef>Flow_0yauk10</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+    </bpmn:task>
+    <bpmn:task id="Activity_12rqerh" name="wait for preference input">
+      <bpmn:incoming>Flow_13jervv</bpmn:incoming>
+    </bpmn:task>
+    <bpmn:task id="Activity_0707to6" name="save preferences">
+      <bpmn:incoming>Flow_1xs1aq5</bpmn:incoming>
+      <bpmn:outgoing>Flow_183a3gl</bpmn:outgoing>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1yqt1l6">
+        <bpmn:targetRef>Flow_0yauk10</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:startEvent id="Event_0pwno12" name="user logs in">
+      <bpmn:outgoing>Flow_0opaylx</bpmn:outgoing>
+    </bpmn:startEvent>
+    <bpmn:exclusiveGateway id="Gateway_0m8yvmm" name="Are all users logged in?">
+      <bpmn:incoming>Flow_0opaylx</bpmn:incoming>
+      <bpmn:outgoing>Flow_1l6p48t</bpmn:outgoing>
+      <bpmn:outgoing>Flow_0wtv4e8</bpmn:outgoing>
+    </bpmn:exclusiveGateway>
+    <bpmn:task id="Activity_1l7kym5" name="wait for all users">
+      <bpmn:incoming>Flow_1l6p48t</bpmn:incoming>
+    </bpmn:task>
+    <bpmn:task id="Activity_0g26qnc" name="select starting state of configuration">
+      <bpmn:incoming>Flow_0wtv4e8</bpmn:incoming>
+      <bpmn:outgoing>Flow_0pmix2e</bpmn:outgoing>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1cxhkzs">
+        <bpmn:targetRef>Flow_00hy6u9</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:intermediateThrowEvent id="Event_08vhdg2" name="user enters preference">
+      <bpmn:outgoing>Flow_1xs1aq5</bpmn:outgoing>
+    </bpmn:intermediateThrowEvent>
+    <bpmn:dataStoreReference id="Flow_0yauk10" name="user preferences" />
+    <bpmn:dataStoreReference id="Flow_00hy6u9" name="current configuration" />
+    <bpmn:dataStoreReference id="Flow_0ug7unw" name="database of finished configurations" />
+    <bpmn:sequenceFlow id="Flow_0opaylx" sourceRef="Event_0pwno12" targetRef="Gateway_0m8yvmm" />
+    <bpmn:sequenceFlow id="Flow_1l6p48t" name="no" sourceRef="Gateway_0m8yvmm" targetRef="Activity_1l7kym5" />
+    <bpmn:sequenceFlow id="Flow_0wtv4e8" name="yes" sourceRef="Gateway_0m8yvmm" targetRef="Activity_0g26qnc" />
+    <bpmn:sequenceFlow id="Flow_0pmix2e" sourceRef="Activity_0g26qnc" targetRef="Gateway_0rs0dwe" />
+    <bpmn:sequenceFlow id="Flow_13jervv" name="no" sourceRef="Gateway_0rs0dwe" targetRef="Activity_12rqerh" />
+    <bpmn:sequenceFlow id="Flow_0sd24xk" name="yes" sourceRef="Gateway_0rs0dwe" targetRef="Activity_1konb1u" />
+    <bpmn:sequenceFlow id="Flow_1xs1aq5" sourceRef="Event_08vhdg2" targetRef="Activity_0707to6" />
+    <bpmn:sequenceFlow id="Flow_183a3gl" sourceRef="Activity_0707to6" targetRef="Gateway_0rs0dwe" />
+    <bpmn:endEvent id="EndEvent_049prfp">
+      <bpmn:incoming>SequenceFlow_14bjmv4</bpmn:incoming>
+    </bpmn:endEvent>
+    <bpmn:sequenceFlow id="SequenceFlow_14bjmv4" sourceRef="Activity_1konb1u" targetRef="EndEvent_049prfp" />
+  </bpmn:process>
+  <bpmndi:BPMNDiagram id="BPMNDiagram_1">
+    <bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_0aaqeef">
+      <bpmndi:BPMNEdge id="Flow_0opaylx_di" bpmnElement="Flow_0opaylx">
+        <di:waypoint x="198" y="210" />
+        <di:waypoint x="285" y="210" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1l6p48t_di" bpmnElement="Flow_1l6p48t">
+        <di:waypoint x="310" y="235" />
+        <di:waypoint x="310" y="270" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="293" y="258" width="13" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0wtv4e8_di" bpmnElement="Flow_0wtv4e8">
+        <di:waypoint x="335" y="210" />
+        <di:waypoint x="390" y="210" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="353" y="192" width="19" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0pmix2e_di" bpmnElement="Flow_0pmix2e">
+        <di:waypoint x="490" y="210" />
+        <di:waypoint x="625" y="210" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_13jervv_di" bpmnElement="Flow_13jervv">
+        <di:waypoint x="667" y="202" />
+        <di:waypoint x="751" y="153" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="695" y="163" width="13" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0sd24xk_di" bpmnElement="Flow_0sd24xk">
+        <di:waypoint x="675" y="210" />
+        <di:waypoint x="750" y="210" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="703" y="192" width="19" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1xs1aq5_di" bpmnElement="Flow_1xs1aq5">
+        <di:waypoint x="558" y="310" />
+        <di:waypoint x="600" y="310" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_183a3gl_di" bpmnElement="Flow_183a3gl">
+        <di:waypoint x="650" y="270" />
+        <di:waypoint x="650" y="235" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNShape id="Gateway_0rs0dwe_di" bpmnElement="Gateway_0rs0dwe" isMarkerVisible="true">
+        <dc:Bounds x="625" y="185" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="613" y="123" width="74" height="53" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_1konb1u_di" bpmnElement="Activity_1konb1u">
+        <dc:Bounds x="750" y="170" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_12rqerh_di" bpmnElement="Activity_12rqerh">
+        <dc:Bounds x="750" y="80" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_0707to6_di" bpmnElement="Activity_0707to6">
+        <dc:Bounds x="600" y="270" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Event_0pwno12_di" bpmnElement="Event_0pwno12">
+        <dc:Bounds x="162" y="192" width="36" height="36" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="152" y="235" width="57" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Gateway_1rbl3uy_di" bpmnElement="Gateway_0m8yvmm" isMarkerVisible="true">
+        <dc:Bounds x="285" y="185" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="278" y="147.5" width="63" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_1l7kym5_di" bpmnElement="Activity_1l7kym5">
+        <dc:Bounds x="260" y="270" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_0g26qnc_di" bpmnElement="Activity_0g26qnc">
+        <dc:Bounds x="390" y="170" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Event_08vhdg2_di" bpmnElement="Event_08vhdg2">
+        <dc:Bounds x="522" y="292" width="36" height="36" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="511" y="254.5" width="57" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_0yauk10_di" bpmnElement="Flow_0yauk10">
+        <dc:Bounds x="725" y="285" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="707" y="342" width="86" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_00hy6u9_di" bpmnElement="Flow_00hy6u9">
+        <dc:Bounds x="775" y="395" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="768" y="452" width="64" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_0ug7unw_di" bpmnElement="Flow_0ug7unw">
+        <dc:Bounds x="829" y="285" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="819" y="342" width="70" height="40" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNEdge id="DataInputAssociation_0oxxzci_di" bpmnElement="DataInputAssociation_0oxxzci">
+        <di:waypoint x="840" y="285" />
+        <di:waypoint x="820" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataInputAssociation_0j2qf1y_di" bpmnElement="DataInputAssociation_0j2qf1y">
+        <di:waypoint x="800" y="395" />
+        <di:waypoint x="800" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataInputAssociation_052ijwq_di" bpmnElement="DataInputAssociation_052ijwq">
+        <di:waypoint x="763" y="285" />
+        <di:waypoint x="780" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataOutputAssociation_1yqt1l6_di" bpmnElement="DataOutputAssociation_1yqt1l6">
+        <di:waypoint x="700" y="310" />
+        <di:waypoint x="725" y="310" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataOutputAssociation_1cxhkzs_di" bpmnElement="DataOutputAssociation_1cxhkzs">
+        <di:waypoint x="440" y="250" />
+        <di:waypoint x="440" y="415" />
+        <di:waypoint x="775" y="415" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNShape id="EndEvent_049prfp_di" bpmnElement="EndEvent_049prfp">
+        <dc:Bounds x="892" y="192" width="36" height="36" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNEdge id="SequenceFlow_14bjmv4_di" bpmnElement="SequenceFlow_14bjmv4">
+        <di:waypoint x="850" y="210" />
+        <di:waypoint x="892" y="210" />
+      </bpmndi:BPMNEdge>
+    </bpmndi:BPMNPlane>
+  </bpmndi:BPMNDiagram>
+</bpmn:definitions>

Additional_Notes/bpmn/configuration_process_continious_recommendation.bpmn

@@ -0,0 +1,195 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<bpmn:definitions xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" id="Definitions_0hcliey" targetNamespace="http://bpmn.io/schema/bpmn" exporter="bpmn-js (https://demo.bpmn.io)" exporterVersion="6.3.0">
+  <bpmn:process id="Process_0aaqeef" isExecutable="false">
+    <bpmn:exclusiveGateway id="Gateway_0rs0dwe" name="Have all preferences of all users been entered?">
+      <bpmn:incoming>Flow_0pmix2e</bpmn:incoming>
+      <bpmn:incoming>Flow_183a3gl</bpmn:incoming>
+      <bpmn:outgoing>Flow_0sd24xk</bpmn:outgoing>
+      <bpmn:outgoing>SequenceFlow_1ygx377</bpmn:outgoing>
+    </bpmn:exclusiveGateway>
+    <bpmn:task id="Activity_1konb1u" name="generate recommenda-tion">
+      <bpmn:incoming>Flow_0sd24xk</bpmn:incoming>
+      <bpmn:property id="Property_03b25wa" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_0oxxzci">
+        <bpmn:sourceRef>Flow_0ug7unw</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataInputAssociation id="DataInputAssociation_0j2qf1y">
+        <bpmn:sourceRef>Flow_00hy6u9</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataInputAssociation id="DataInputAssociation_052ijwq">
+        <bpmn:sourceRef>Flow_0yauk10</bpmn:sourceRef>
+        <bpmn:targetRef>Property_03b25wa</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+    </bpmn:task>
+    <bpmn:task id="Activity_0707to6" name="save preferences">
+      <bpmn:incoming>Flow_1xs1aq5</bpmn:incoming>
+      <bpmn:outgoing>Flow_183a3gl</bpmn:outgoing>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1yqt1l6">
+        <bpmn:targetRef>Flow_0yauk10</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:startEvent id="Event_0pwno12" name="user logs in">
+      <bpmn:outgoing>Flow_0opaylx</bpmn:outgoing>
+    </bpmn:startEvent>
+    <bpmn:exclusiveGateway id="Gateway_0m8yvmm" name="Are all users logged in?">
+      <bpmn:incoming>Flow_0opaylx</bpmn:incoming>
+      <bpmn:outgoing>Flow_1l6p48t</bpmn:outgoing>
+      <bpmn:outgoing>Flow_0wtv4e8</bpmn:outgoing>
+    </bpmn:exclusiveGateway>
+    <bpmn:task id="Activity_1l7kym5" name="wait for all users">
+      <bpmn:incoming>Flow_1l6p48t</bpmn:incoming>
+    </bpmn:task>
+    <bpmn:task id="Activity_0g26qnc" name="select starting state of configuration">
+      <bpmn:incoming>Flow_0wtv4e8</bpmn:incoming>
+      <bpmn:outgoing>Flow_0pmix2e</bpmn:outgoing>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1cxhkzs">
+        <bpmn:targetRef>Flow_00hy6u9</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:intermediateThrowEvent id="Event_08vhdg2" name="user enters preference">
+      <bpmn:outgoing>Flow_1xs1aq5</bpmn:outgoing>
+    </bpmn:intermediateThrowEvent>
+    <bpmn:dataStoreReference id="Flow_0yauk10" name="user preferences" />
+    <bpmn:dataStoreReference id="Flow_00hy6u9" name="current configuration" />
+    <bpmn:dataStoreReference id="Flow_0ug7unw" name="database of finished configurations" />
+    <bpmn:sequenceFlow id="Flow_0opaylx" sourceRef="Event_0pwno12" targetRef="Gateway_0m8yvmm" />
+    <bpmn:sequenceFlow id="Flow_1l6p48t" name="no" sourceRef="Gateway_0m8yvmm" targetRef="Activity_1l7kym5" />
+    <bpmn:sequenceFlow id="Flow_0wtv4e8" name="yes" sourceRef="Gateway_0m8yvmm" targetRef="Activity_0g26qnc" />
+    <bpmn:sequenceFlow id="Flow_0pmix2e" sourceRef="Activity_0g26qnc" targetRef="Gateway_0rs0dwe" />
+    <bpmn:sequenceFlow id="Flow_0sd24xk" name="yes" sourceRef="Gateway_0rs0dwe" targetRef="Activity_1konb1u" />
+    <bpmn:sequenceFlow id="Flow_1xs1aq5" sourceRef="Event_08vhdg2" targetRef="Activity_0707to6" />
+    <bpmn:sequenceFlow id="Flow_183a3gl" sourceRef="Activity_0707to6" targetRef="Gateway_0rs0dwe" />
+    <bpmn:endEvent id="EndEvent_04vuhjz">
+      <bpmn:incoming>SequenceFlow_1ygx377</bpmn:incoming>
+    </bpmn:endEvent>
+    <bpmn:sequenceFlow id="SequenceFlow_1ygx377" name="no" sourceRef="Gateway_0rs0dwe" targetRef="EndEvent_04vuhjz" />
+  </bpmn:process>
+  <bpmndi:BPMNDiagram id="BPMNDiagram_1">
+    <bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_0aaqeef">
+      <bpmndi:BPMNEdge id="Flow_0opaylx_di" bpmnElement="Flow_0opaylx">
+        <di:waypoint x="338" y="210" />
+        <di:waypoint x="365" y="210" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1l6p48t_di" bpmnElement="Flow_1l6p48t">
+        <di:waypoint x="390" y="235" />
+        <di:waypoint x="390" y="270" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="373" y="236" width="13" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0wtv4e8_di" bpmnElement="Flow_0wtv4e8">
+        <di:waypoint x="415" y="210" />
+        <di:waypoint x="460" y="210" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="429" y="192" width="17" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0pmix2e_di" bpmnElement="Flow_0pmix2e">
+        <di:waypoint x="560" y="210" />
+        <di:waypoint x="625" y="210" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0sd24xk_di" bpmnElement="Flow_0sd24xk">
+        <di:waypoint x="675" y="210" />
+        <di:waypoint x="750" y="210" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="704" y="192" width="17" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1xs1aq5_di" bpmnElement="Flow_1xs1aq5">
+        <di:waypoint x="578" y="310" />
+        <di:waypoint x="600" y="310" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_183a3gl_di" bpmnElement="Flow_183a3gl">
+        <di:waypoint x="650" y="270" />
+        <di:waypoint x="650" y="235" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNShape id="Gateway_0rs0dwe_di" bpmnElement="Gateway_0rs0dwe" isMarkerVisible="true">
+        <dc:Bounds x="625" y="185" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="608" y="123" width="85" height="53" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_1konb1u_di" bpmnElement="Activity_1konb1u">
+        <dc:Bounds x="750" y="170" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_0707to6_di" bpmnElement="Activity_0707to6">
+        <dc:Bounds x="600" y="270" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Event_0pwno12_di" bpmnElement="Event_0pwno12">
+        <dc:Bounds x="302" y="192" width="36" height="36" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="292" y="235" width="57" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Gateway_1rbl3uy_di" bpmnElement="Gateway_0m8yvmm" isMarkerVisible="true">
+        <dc:Bounds x="365" y="185" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="359" y="148" width="61" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_1l7kym5_di" bpmnElement="Activity_1l7kym5">
+        <dc:Bounds x="340" y="270" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_0g26qnc_di" bpmnElement="Activity_0g26qnc">
+        <dc:Bounds x="460" y="170" width="100" height="80" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Event_08vhdg2_di" bpmnElement="Event_08vhdg2">
+        <dc:Bounds x="542" y="292" width="36" height="36" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="532" y="335" width="56" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_0yauk10_di" bpmnElement="Flow_0yauk10">
+        <dc:Bounds x="725" y="285" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="707" y="342" width="86" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_00hy6u9_di" bpmnElement="Flow_00hy6u9">
+        <dc:Bounds x="775" y="395" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="768" y="452" width="64" height="27" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Flow_0ug7unw_di" bpmnElement="Flow_0ug7unw">
+        <dc:Bounds x="829" y="285" width="50" height="50" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="819" y="342" width="70" height="40" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNEdge id="DataInputAssociation_0oxxzci_di" bpmnElement="DataInputAssociation_0oxxzci">
+        <di:waypoint x="840" y="285" />
+        <di:waypoint x="820" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataInputAssociation_0j2qf1y_di" bpmnElement="DataInputAssociation_0j2qf1y">
+        <di:waypoint x="800" y="395" />
+        <di:waypoint x="800" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataInputAssociation_052ijwq_di" bpmnElement="DataInputAssociation_052ijwq">
+        <di:waypoint x="763" y="285" />
+        <di:waypoint x="780" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataOutputAssociation_1yqt1l6_di" bpmnElement="DataOutputAssociation_1yqt1l6">
+        <di:waypoint x="700" y="310" />
+        <di:waypoint x="725" y="310" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataOutputAssociation_1cxhkzs_di" bpmnElement="DataOutputAssociation_1cxhkzs">
+        <di:waypoint x="510" y="250" />
+        <di:waypoint x="510" y="415" />
+        <di:waypoint x="775" y="415" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNShape id="EndEvent_04vuhjz_di" bpmnElement="EndEvent_04vuhjz">
+        <dc:Bounds x="762" y="112" width="36" height="36" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNEdge id="SequenceFlow_1ygx377_di" bpmnElement="SequenceFlow_1ygx377">
+        <di:waypoint x="666" y="201" />
+        <di:waypoint x="765" y="140" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="709" y="154" width="13" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNEdge>
+    </bpmndi:BPMNPlane>
+  </bpmndi:BPMNDiagram>
+</bpmn:definitions>

Additional_Notes/bpmn/generating_evaluation_input.bpmn

@@ -0,0 +1,218 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<bpmn:definitions xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" id="Definitions_17iqhxr" targetNamespace="http://bpmn.io/schema/bpmn" exporter="bpmn-js (https://demo.bpmn.io)" exporterVersion="6.5.0-kah.1">
+  <bpmn:process id="Process_0lzf9jk" isExecutable="false">
+    <bpmn:dataObjectReference id="Flow_0dc8ucf" name="group member profiles" dataObjectRef="Flow_0majyqj" />
+    <bpmn:dataObject id="Flow_0majyqj" />
+    <bpmn:task id="Activity_0el490h" name="generate groups">
+      <bpmn:incoming>Flow_0wgvs43</bpmn:incoming>
+      <bpmn:outgoing>Flow_1hdpd6e</bpmn:outgoing>
+      <bpmn:property id="Property_0fzi3ej" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_0rzl9pn">
+        <bpmn:sourceRef>Flow_0dc8ucf</bpmn:sourceRef>
+        <bpmn:targetRef>Property_0fzi3ej</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1h1k7f3">
+        <bpmn:targetRef>DataObjectReference_0dk8ord</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:task id="Activity_12shstq" name="generate unfinished configurations">
+      <bpmn:incoming>Flow_18ah258</bpmn:incoming>
+      <bpmn:outgoing>Flow_08qskbu</bpmn:outgoing>
+      <bpmn:property id="Property_0llmkr1" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_1k14ujc">
+        <bpmn:sourceRef>DataStoreReference_0e449rj</bpmn:sourceRef>
+        <bpmn:targetRef>Property_0llmkr1</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_1wqdnca">
+        <bpmn:targetRef>DataObjectReference_0b8tret</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:dataStoreReference id="DataStoreReference_0e449rj" name="finished configuration database" />
+    <bpmn:dataObjectReference id="DataObjectReference_0b8tret" name="unfinished configurations" dataObjectRef="DataObject_0j65hg4" />
+    <bpmn:dataObject id="DataObject_0j65hg4" />
+    <bpmn:dataObjectReference id="DataObjectReference_0dk8ord" name="preferences&#10;(for each group)" dataObjectRef="DataObject_1kmiddh" />
+    <bpmn:dataObject id="DataObject_1kmiddh" />
+    <bpmn:startEvent id="Event_0e7vhme">
+      <bpmn:outgoing>Flow_1m2t9yp</bpmn:outgoing>
+    </bpmn:startEvent>
+    <bpmn:parallelGateway id="Gateway_0dm9jmc">
+      <bpmn:incoming>Flow_1m2t9yp</bpmn:incoming>
+      <bpmn:outgoing>Flow_0wgvs43</bpmn:outgoing>
+      <bpmn:outgoing>Flow_18ah258</bpmn:outgoing>
+    </bpmn:parallelGateway>
+    <bpmn:sequenceFlow id="Flow_1m2t9yp" sourceRef="Event_0e7vhme" targetRef="Gateway_0dm9jmc" />
+    <bpmn:task id="Activity_033bobt" name="pair up preferences and unfinished configurations">
+      <bpmn:incoming>Flow_1mts5v8</bpmn:incoming>
+      <bpmn:outgoing>Flow_0vx10vl</bpmn:outgoing>
+      <bpmn:property id="Property_0e1efvb" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_097tm6g">
+        <bpmn:sourceRef>DataObjectReference_0b8tret</bpmn:sourceRef>
+        <bpmn:targetRef>Property_0e1efvb</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataInputAssociation id="DataInputAssociation_16303zg">
+        <bpmn:sourceRef>DataObjectReference_0dk8ord</bpmn:sourceRef>
+        <bpmn:targetRef>Property_0e1efvb</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+      <bpmn:dataOutputAssociation id="DataOutputAssociation_0frorp4">
+        <bpmn:targetRef>DataObjectReference_125w85o</bpmn:targetRef>
+      </bpmn:dataOutputAssociation>
+    </bpmn:task>
+    <bpmn:dataObjectReference id="DataObjectReference_125w85o" name="pairs of preferences and unfinished configurations" dataObjectRef="DataObject_0pycepc" />
+    <bpmn:dataObject id="DataObject_0pycepc" />
+    <bpmn:sequenceFlow id="Flow_0wgvs43" sourceRef="Gateway_0dm9jmc" targetRef="Activity_0el490h" />
+    <bpmn:sequenceFlow id="Flow_18ah258" sourceRef="Gateway_0dm9jmc" targetRef="Activity_12shstq" />
+    <bpmn:endEvent id="Event_02lc079">
+      <bpmn:incoming>Flow_0vx10vl</bpmn:incoming>
+      <bpmn:property id="Property_18jxcbg" name="__targetRef_placeholder" />
+      <bpmn:dataInputAssociation id="DataInputAssociation_1txvcxy">
+        <bpmn:sourceRef>DataObjectReference_125w85o</bpmn:sourceRef>
+        <bpmn:targetRef>Property_18jxcbg</bpmn:targetRef>
+      </bpmn:dataInputAssociation>
+    </bpmn:endEvent>
+    <bpmn:sequenceFlow id="Flow_0vx10vl" sourceRef="Activity_033bobt" targetRef="Event_02lc079" />
+    <bpmn:parallelGateway id="Gateway_0vlb15w">
+      <bpmn:incoming>Flow_1hdpd6e</bpmn:incoming>
+      <bpmn:incoming>Flow_08qskbu</bpmn:incoming>
+      <bpmn:outgoing>Flow_1mts5v8</bpmn:outgoing>
+    </bpmn:parallelGateway>
+    <bpmn:sequenceFlow id="Flow_1hdpd6e" sourceRef="Activity_0el490h" targetRef="Gateway_0vlb15w" />
+    <bpmn:sequenceFlow id="Flow_08qskbu" sourceRef="Activity_12shstq" targetRef="Gateway_0vlb15w" />
+    <bpmn:sequenceFlow id="Flow_1mts5v8" sourceRef="Gateway_0vlb15w" targetRef="Activity_033bobt" />
+  </bpmn:process>
+  <bpmndi:BPMNDiagram id="BPMNDiagram_1">
+    <bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_0lzf9jk">
+      <bpmndi:BPMNEdge id="Flow_1m2t9yp_di" bpmnElement="Flow_1m2t9yp">
+        <di:waypoint x="188" y="250" />
+        <di:waypoint x="225" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0wgvs43_di" bpmnElement="Flow_0wgvs43">
+        <di:waypoint x="250" y="225" />
+        <di:waypoint x="250" y="200" />
+        <di:waypoint x="290" y="200" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_18ah258_di" bpmnElement="Flow_18ah258">
+        <di:waypoint x="250" y="275" />
+        <di:waypoint x="250" y="300" />
+        <di:waypoint x="290" y="300" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_0vx10vl_di" bpmnElement="Flow_0vx10vl">
+        <di:waypoint x="590" y="250" />
+        <di:waypoint x="621" y="250" />
+        <di:waypoint x="621" y="170" />
+        <di:waypoint x="652" y="170" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1hdpd6e_di" bpmnElement="Flow_1hdpd6e">
+        <di:waypoint x="390" y="200" />
+        <di:waypoint x="430" y="200" />
+        <di:waypoint x="430" y="225" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_08qskbu_di" bpmnElement="Flow_08qskbu">
+        <di:waypoint x="390" y="300" />
+        <di:waypoint x="430" y="300" />
+        <di:waypoint x="430" y="275" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="Flow_1mts5v8_di" bpmnElement="Flow_1mts5v8">
+        <di:waypoint x="455" y="250" />
+        <di:waypoint x="490" y="250" />
+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNShape id="Activity_0el490h_di" bpmnElement="Activity_0el490h">
+        <dc:Bounds x="290" y="160" width="100" height="80" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="299" y="233" width="81" height="14" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_12shstq_di" bpmnElement="Activity_12shstq">
+        <dc:Bounds x="290" y="260" width="100" height="80" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="305" y="330" width="70" height="40" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Gateway_11n50k1_di" bpmnElement="Gateway_0dm9jmc">
+        <dc:Bounds x="225" y="225" width="50" height="50" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Event_0e7vhme_di" bpmnElement="Event_0e7vhme">
+        <dc:Bounds x="152" y="232" width="36" height="36" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Gateway_0vlb15w_di" bpmnElement="Gateway_0vlb15w">
+        <dc:Bounds x="405" y="225" width="50" height="50" />
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="Activity_033bobt_di" bpmnElement="Activity_033bobt">
+        <dc:Bounds x="490" y="210" width="100" height="80" />
+        <bpmndi:BPMNLabel>
+          <dc:Bounds x="499" y="203" width="82" height="53" />
+        </bpmndi:BPMNLabel>
+      </bpmndi:BPMNShape>
+      <bpmndi:BPMNShape id="DataObjectReference_0dk8ord_di" bpmnElement="DataObjectReference_0dk8ord">
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+      <bpmndi:BPMNEdge id="DataInputAssociation_0rzl9pn_di" bpmnElement="DataInputAssociation_0rzl9pn">
+        <di:waypoint x="238" y="140" />
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+      </bpmndi:BPMNEdge>
+      <bpmndi:BPMNEdge id="DataInputAssociation_16303zg_di" bpmnElement="DataInputAssociation_16303zg">
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+      <bpmndi:BPMNEdge id="DataOutputAssociation_0frorp4_di" bpmnElement="DataOutputAssociation_0frorp4">
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+    </bpmndi:BPMNPlane>
+  </bpmndi:BPMNDiagram>
+</bpmn:definitions>

Additional_Notes/draw.io/MerlinCollabRecommender.drawio

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Additional_Notes/draw.io/MerlinCollabRecommender_nocomponent.drawio

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