add citation for group recommender

01_Topic/topic.bib

@@ -927,4 +927,22 @@ OCLC: 935904837}
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9MQQP8WF\\Knijnenburg et al_2011_Each to his own.pdf}
 }
 
+@incollection{jamesonRecommendationGroups2007,
+  address = {{Berlin, Heidelberg}},
+  series = {Lecture {{Notes}} in {{Computer Science}}},
+  title = {Recommendation to {{Groups}}},
+  isbn = {978-3-540-72079-9},
+  abstract = {Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user's preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.},
+  language = {en},
+  booktitle = {The {{Adaptive Web}}: {{Methods}} and {{Strategies}} of {{Web Personalization}}},
+  publisher = {{Springer}},
+  author = {Jameson, Anthony and Smyth, Barry},
+  editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang},
+  year = {2007},
+  keywords = {Animated Character,Explicit Preference,Group Recommender,Individual Group Member,Recommender System},
+  pages = {596-627},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Q9DL2M5M\\Jameson_Smyth_2007_Recommendation to Groups.pdf},
+  doi = {10.1007/978-3-540-72079-9_20}
+}
+
 

02_Proposal/proposal.bib

@@ -927,4 +927,22 @@ OCLC: 935904837}
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\9MQQP8WF\\Knijnenburg et al_2011_Each to his own.pdf}
 }
 
+@incollection{jamesonRecommendationGroups2007,
+  address = {{Berlin, Heidelberg}},
+  series = {Lecture {{Notes}} in {{Computer Science}}},
+  title = {Recommendation to {{Groups}}},
+  isbn = {978-3-540-72079-9},
+  abstract = {Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user's preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.},
+  language = {en},
+  booktitle = {The {{Adaptive Web}}: {{Methods}} and {{Strategies}} of {{Web Personalization}}},
+  publisher = {{Springer}},
+  author = {Jameson, Anthony and Smyth, Barry},
+  editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang},
+  year = {2007},
+  keywords = {Animated Character,Explicit Preference,Group Recommender,Individual Group Member,Recommender System},
+  pages = {596-627},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Q9DL2M5M\\Jameson_Smyth_2007_Recommendation to Groups.pdf},
+  doi = {10.1007/978-3-540-72079-9_20}
+}
+
 

02_Proposal/sections/10_foundations.tex

@@ -26,8 +26,7 @@ A hybrid recommender combines different recommendation approaches to use the str
 
 \section{Group Recommender System}
 
-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 this model recommendation strategies as described in \ref{sec:Foundations:RecommenderSystem} can be used to generate recommendations.
+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 this model recommendation strategies as described in \ref{sec:Foundations:RecommenderSystem} can be used to generate recommendations \cite{jamesonRecommendationGroups2007}.
-%TODO: citations for group recommender system
 
 \section{Product Configuration}
 \label{sec:Foundations:ProductConfiguration}