A visual interface for critiquing-based recommender systems
Proceedings of the 9th ACM conference on Electronic commerce
SFViz: interest-based friends exploration and recommendation in social networks
Proceedings of the 2011 Visual Information Communication - International Symposium
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
Each to his own: how different users call for different interaction methods in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Beyond lists: studying the effect of different recommendation visualizations
Proceedings of the sixth ACM conference on Recommender systems
Smallworlds: visualizing social recommendations
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Visualizing recommendations to support exploration, transparency and controllability
Proceedings of the 2013 international conference on Intelligent user interfaces
See what you want to see: visual user-driven approach for hybrid recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
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In this paper, we contribute to the study of recommender systems from a HCI perspective by investigating the effects upon the user experience of a novel interface which uses a Venn diagram to represent the outputs of an interactive talk recommender system. We present the results of a preliminary user study on talk recommendations in the context of a conference with n=37 people that used our system under one of two conditions: a static list of recommendations, or the enhanced visual controllable interface. The user behavioral analysis and the results of a survey that n=17 users answered provide interesting insights for designers and developers of interfaces for recommender systems, especially when the items can have one or more contexts of relevancy as in a hybrid recommender system.