IEEE Transactions on Pattern Analysis and Machine Intelligence
MUSICFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Combining knowledge from different sources in causal probabilistic models
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Information markets vs. opinion pools: an empirical comparison
Proceedings of the 6th ACM conference on Electronic commerce
Group recommender systems: a critiquing based approach
Proceedings of the 11th international conference on Intelligent user interfaces
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Graphical Models for Groups: Belief Aggregation and Risk Sharing
Decision Analysis
Group Recommending: A methodological Approach based on Bayesian Networks
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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In this paper we focus on the problem of belief aggregation, i.e. the task of forming a group consensus probability distribution by combining the beliefs of the individual members of the group. We propose the use of Bayesian Networks to model the interactions between the individuals of the group and introduce average and majority canonical models and their application to information aggregation. Due to efficiency restrictions imposed by the Group Recommending problem, where our research is framed, we have had to develop specific inference algorithms to compute group recommendations.