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add related work that is from papers

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35
02_Proposal/proposal.bib
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@@ -187,6 +187,23 @@
title = {Silence Is Golden: Team Problem Solving and Communication Costs}
}
@inproceedings{chenEmpatheticonsDesigningEmotion2014,
abstract = {Group recommender systems help users to find items of interest collaboratively. Support for such collaboration has been mainly provided by tools that visualize membership awareness, preference awareness and decision awareness. However, these mechanisms do not address group dynamic issues: how member may affect each other. In this paper, we investigate the roles of emotion awareness tools and how they may enable positive group dynamics. We first describe the design process behind a set of dynamic emoticons, which we call empatheticons. We then show that they allow users to represent, annotate, and visualize group members' emotions in GroupFun, a group music recommender. An in-depth user study (N = 18) with GroupFun demonstrates that users' emotion annotation for recommended songs can be influenced by other group members. Most importantly, empatheticons enhance users' perceptions of the connectedness (immediacy) and familiarity (intimacy) with each other and the positive group dynamics.},
author = {Chen, Yu and Ma, Xiaojuan and Cerezo, Alfredo and Pu, Pearl},
booktitle = {Proceedings of the {{XV International Conference}} on {{Human Computer Interaction}} - {{Interacci\'on}} '14},
date = {2014},
doi = {10.1145/2662253.2662269},
eventtitle = {The {{XV International Conference}}},
file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\5U62TD2Q\\Chen et al. - 2014 - Empatheticons Designing Emotion Awareness Tools f.pdf},
isbn = {978-1-4503-2880-7},
langid = {english},
location = {{Puerto de la Cruz, Tenerife, Spain}},
pages = {1-8},
publisher = {{ACM Press}},
shorttitle = {Empatheticons},
title = {Empatheticons: {{Designing Emotion Awareness Tools}} for {{Group Recommenders}}}
}
@inproceedings{chenInterfaceInteractionDesign2011,
abstract = {Group and social recommender systems aim to recommend items of interest to a group or a community of people. The user issues in such systems cannot be addressed by examining the satisfaction of their members as individuals. Rather, group satisfaction should be studied as a result of the interaction and interface methods that support group dynamics and interaction. In this paper, we survey the state-of-the-art in user experience design of group and social recommender systems. We further apply the techniques used in the current recommender systems to GroupFun, a music social group recommender system. After presenting the interface and interaction characteristics of GroupFun, we further analyze the design space and propose areas for future research in pursuit of an affective recommender.},
author = {Chen, Yu},
@@ -319,6 +336,22 @@
volume = {73}
}
@article{falknerRecommendationTechnologiesConfigurable2011,
abstract = {State of the art recommender systems support users in the selection of items from a predefined assortment (e.g., movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are represented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to non-configurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this paper we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.},
author = {Falkner, Andreas and Felfernig, Alexander and Haag, Albert},
date = {2011-10-31},
doi = {10.1609/aimag.v32i3.2369},
file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4QVSX5DQ\\Falkner et al. - 2011 - Recommendation Technologies for Configurable Produ.pdf},
issn = {0738-4602, 0738-4602},
journaltitle = {AI Magazine},
langid = {english},
number = {3},
pages = {99},
shortjournal = {AIMag},
title = {Recommendation {{Technologies}} for {{Configurable Products}}},
volume = {32}
}
@incollection{felfernigBiasesGroupDecisions2018,
abstract = {Decision biases can be interpreted as tendencies to think and act in specific ways that result in a systematic deviation of potentially rational and high-quality decisions. In this chapter, we provide an overview of example decision biases and show possibilities to counteract these. The overview includes (1) biases that exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).},
author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
@@ -692,7 +725,7 @@
number = {2},
pages = {69-82},
shortjournal = {JSW},
title = {Collaborative {{Product Configuration}}:},
title = {Collaborative {{Product Configuration}}},
volume = {3}
}

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02_Proposal/sections/20_related_work.tex
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@@ -1,21 +1,28 @@
\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. 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\cite{choudharyMulticriteriaGroupRecommender2020}.
\todo[author=Robert, inline]{Bei der related work bezieht bisher noch Abschlussarbeiten von Karlsruher Hochschulen mit ein. Hier sollten noch wissenschaftliche Papiere diskutiert werden.}
\todo[inline]{add a few books and papers to related work}
\item[\citeauthor{chenInterfaceInteractionDesign2011}] looks at interface and interaction designs that supports the overall group and does not only consider each user individually. Chen design 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. 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 group decision support \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}.
\item[\citeauthor{felferningGroupBasedConfiguration2016}] introduces basic definitions of group based configuration tasks, shows 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. Hereby each user configures according to their own preferences and the system proposes a configuration according to different strategies \cite{velasquez-guevaraMultiSPLOTSupportingMultiuser2018}.
\end{description}
\section{Recommender Systems for Configuration}
\label{sec:Related_Work:RecommenderSystemsForGonfiguration}
\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. His prototype combines constraint-based filtering with collaborative filtering \cite{rubinshteynEntwicklungHybridenRecommender2018}.
@@ -24,6 +31,8 @@
\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 combines with nearest neighbour to find configurations for specific users \cite{ullmannEntwurfUndUmsetzung2017}. \par
\item[\citeauthor{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 \cite{wetzelPersonalisierterUndLernender2017}.
\item[\citeauthor{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 \cite{falknerRecommendationTechnologiesConfigurable2011}.
\end{description}
\section{Group Dynamics and Bias}