add related work that is from papers

02_Proposal/proposal.bib

@@ -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}
 }