Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Human–Computer Interaction and Global Development
Foundations and Trends in Human-Computer Interaction
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We present the Visual Decision Maker (VDM), an application that gives movie recommendations to groups of people sitting together. The VDM provides a TV like user experience: a stream of movie stills flows towards the center of the screen, and users press buttons on remote controls to vote on the currently selected movie. A collaborative filtering engine provides recommendations for each user and for the group as a whole based on the votes. Three principles guided our design of the VDM: shared focus, dynamic pacing, and encouraging conversations. In this paper we present the results of a four month public installation and a lab study showing how these design choices affected people's usage and people's experience of the VDM. Our results show that shared focus is important for users to feel that the group's tastes are represented in the recommendations.