Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
MUSICFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
User profiling with Case-Based Reasoning and Bayesian Networks
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
IEEE Transactions on Knowledge and Data Engineering
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
Introduction to the special issue on statistical and probabilistic methods for user modeling
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
A group recommendation system with consideration of interactions among group members
Expert Systems with Applications: An International Journal
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Group decision making through mediated discussions
UM'03 Proceedings of the 9th international conference on User modeling
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Iterative voting under uncertainty for group recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Enhancing group recommendation by incorporating social relationship interactions
Proceedings of the 16th ACM international conference on Supporting group work
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Design guidelines for mobile group recommender systems to handle inaccurate or missing location data
Proceedings of the fifth ACM conference on Recommender systems
Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
A latent model for collaborative filtering
International Journal of Approximate Reasoning
Using past-prediction accuracy in recommender systems
Information Sciences: an International Journal
A negotiation framework for heterogeneous group recommendation
Expert Systems with Applications: An International Journal
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While the problem of building recommender systems has attracted considerableattention in recent years, most recommender systems are designed for recommendingitems to individuals. The aim of this paper is to automatically recommenda ranked list of new items to a group of users. We will investigate the value of usingBayesian networks to represent the different uncertainties involved in a group recommendingprocess, i.e. those uncertainties related to mechanisms that govern theinteractions between group members and the processes leading to the final choice orrecommendation. We will also show how the most common aggregation strategiesmight be encoded using a Bayesian network formalism. The proposed model can beconsidered as a collaborative Bayesian network-based group recommender system,where group ratings are computed from the past voting patterns of other users withsimilar tastes.