finalize content foundation chapter

30_Thesis/sections/10_foundations.tex

@@ -1,8 +1,7 @@
 \chapter{Foundations}
 \label{ch:Foundations}
 
-This chapter gives an overview over the foundations this thesis is based upon.
-\todo[inline]{write introduction paragraph which tells the order things are introduced in.}
+This chapter gives an overview over the foundations this thesis is based upon. It introduces product configuration, shows how group based product configuration looks like, goes over recommender systems and basic approaches, describes group recommenders and introduces the prototype that is extended by this thesis.
 
 \section{Product Configuration}
 \label{sec:Foundations:ProductConfiguration}
@@ -40,6 +39,8 @@ A recommender system is a system that gives individualized recommendations to us
 
 There are several approaches to recommender systems presented in \cite{felfernigGroupRecommenderSystems2018}, some of them are: collaborative filtering, constraint-based recommendation, content-based filtering and hybrid recommendation.
 
+Advantages and disadvantages of 
+
 \begin{table}
     \centering    
     \begin{tabular}{ l | c | c | c | c | c }
@@ -102,25 +103,7 @@ Using the example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
     \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}
 \end{table}
 
-\subsubsection{Advantages over Collaborative Filtering}
-\todo[inline]{write as text and not bullet points}
-\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}
-\todo[inline]{write as text and not bullet points}
-\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}
-
+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 constrained-based recommendation.
 
 \begin{table}
     \begin{center}
@@ -173,6 +156,18 @@ Using the example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
     \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 most known problems for recommender systems. It occurs, when there is sparse information for users or items. In the case of collaborative filtering this issue exists for both items and users. Content based filtering does not have that issue with items as items are classified based on similarity to other items. The user cold start problem however still persists as a new user has not rated any items therefore no similar items can be recommended. 
+Another common issue is the \emph{grey sheep problem} \citeauthor{grasIdentifyingGreySheep2016} \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. 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 she liked, not similar users.
+Usually, the need for domain knowledge is a disadvantage. However, as product configuration already has domain knowledge baked in to describe feature and how they can be combined here this is not a disadvantage and can even be seen as an advantage. Therefore, domain knowledge can directly be used and does not first need to be learned indirectly.
+Additionally, a collaborative filtering 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. Therefore, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example if a user input into a system does not accurately reflect what they actually like.
+
+\subsubsection{Advantages over Constrained-Based Recommendation}
+
+In constrained-based recommendation approaches it is possible that constraints lead to no possible solution \cite[~ p. 44]{felfernigAlgorithmsGroupRecommendation2018}. This then requires further techniques of constrained relaxing and a user is faced with the situation that he has 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.
+
 \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}.
 
@@ -204,7 +199,7 @@ Both the approach of merging preferences and the approach of using individual us
 \section{Base Recommender System}
 \label{sec:Foundations:BaseSystem}
 
-\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019}'s \cite{raabKollaborativeProduktkonfigurationEchtzeit2019} extends CAS Merlin Configurator in his thesis to allow simultaneous configuration. The extended architecture is shown in \autoref{fig:DesignImplementation:CollaborativeConfiguratorMerlin}.
+\citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} \cite{raabKollaborativeProduktkonfigurationEchtzeit2019} 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{figure}

30_Thesis/thesis.bib

@@ -445,6 +445,7 @@
   location = {{Cham}},
   doi = {10.1007/978-3-319-75067-5_2},
   abstract = {In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4JL9AY5X\\Felfernig et al_2018_Algorithms for Group Recommendation.pdf},
   isbn = {978-3-319-75067-5},
   langid = {english},
   series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
@@ -693,6 +694,23 @@
   number = {2}
 }
 
+@inproceedings{grasIdentifyingGreySheep2016,
+  title = {Identifying {{Grey Sheep Users}} in {{Collaborative Filtering}}: {{A Distribution}}-{{Based Technique}}},
+  shorttitle = {Identifying {{Grey Sheep Users}} in {{Collaborative Filtering}}},
+  booktitle = {Proceedings of the 2016 {{Conference}} on {{User Modeling Adaptation}} and {{Personalization}}},
+  author = {Gras, Benjamin and Brun, Armelle and Boyer, Anne},
+  date = {2016-07-13},
+  pages = {17--26},
+  publisher = {{Association for Computing Machinery}},
+  location = {{Halifax, Nova Scotia, Canada}},
+  doi = {10.1145/2930238.2930242},
+  abstract = {The collaborative filtering (CF) approach in recommender systems assumes that users' preferences are consistent among users. Although accurate, this approach fails on some users. We presume that some of these users belong to a small community of users who have unusual preferences, such users are not compliant with the CF underlying assumption. They are grey sheep users. This paper aims at accurately identifying grey sheep users. We introduce a new distribution-based grey sheep users identification technique, that borrows from outlier detection and from information retrieval, while taking into account the specificities of preference data on which CF relies: extreme sparsity, imprecision and users' bias. The experimental evaluation conducted on a state-of-the-art dataset shows that this new distribution-based technique outperforms state-of-the-art grey sheep users identification techniques.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5RQPTIUR\\Gras et al_2016_Identifying Grey Sheep Users in Collaborative Filtering.pdf},
+  isbn = {978-1-4503-4368-8},
+  keywords = {collaborative filtering,grey sheep users,outlier detection,recommender systems},
+  series = {{{UMAP}} '16}
+}
+
 @article{haagProductConfigurationDecision2011,
   title = {Product Configuration as Decision Support: {{The}} Declarative Paradigm in Practice},
   shorttitle = {Product Configuration as Decision Support},
@@ -911,6 +929,23 @@ procedure.},
   langid = {english}
 }
 
+@article{likaFacingColdStart2014,
+  title = {Facing the Cold Start Problem in Recommender Systems},
+  author = {Lika, Blerina and Kolomvatsos, Kostas and Hadjiefthymiades, Stathes},
+  date = {2014-03-01},
+  journaltitle = {Expert Systems with Applications},
+  shortjournal = {Expert Systems with Applications},
+  volume = {41},
+  pages = {2065--2073},
+  issn = {0957-4174},
+  doi = {10.1016/j.eswa.2013.09.005},
+  abstract = {A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\MLFV7EWY\\Lika et al_2014_Facing the cold start problem in recommender systems.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\CW4M95HK\\S0957417413007240.html},
+  issue = {4, Part 2},
+  keywords = {Cold start problem,Recommender systems},
+  langid = {english}
+}
+
 @inproceedings{liuCGSPAComprehensiveGroup2019,
   title = {{{CGSPA}}: {{Comprehensive Group Similarity Preference Aggregation Algorithm}} for {{Group Itinerary Recommendation System}}},
   shorttitle = {{{CGSPA}}},