Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PeerChooser: visual interactive recommendation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A visual interface for critiquing-based recommender systems
Proceedings of the 9th ACM conference on Electronic commerce
Behavior-driven visualization recommendation
Proceedings of the 14th international conference on Intelligent user interfaces
Search User Interfaces
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
SFViz: interest-based friends exploration and recommendation in social networks
Proceedings of the 2011 Visual Information Communication - International Symposium
Each to his own: how different users call for different interaction methods in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Hierarchically animated transitions in visualizations of tree structures
Proceedings of the International Working Conference on Advanced Visual Interfaces
A field study of a visual controllable talk recommender
Proceedings of the 2013 Chilean Conference on Human - Computer Interaction
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Recommendation Systems have been studied from several perspectives over the last twenty years --prediction accuracy, algorithmic scalability, knowledge sources, types of recommended items and tasks, evaluation methods, etc.-- but one area that has not been deeply investigated is the effect of different visualizations and their interaction with personal traits on users' evaluation of the recommended items. In this paper, I survey visual approaches that go beyond presenting the recommended items as a textual list or as annotations in context. I also review related literature from recommendations' explanations. In this thesis, I aim to understand how different visualizations and some personal traits might influence users' assessment of recommended items, particularly in domains where multidimensional data or contextual constraints are involved. I present the prototype of 2 recommendation visualizations and then briefly propose the research approach of this investigation.