Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
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
Collaborative filtering using interval estimation naïve Bayes
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
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Recommendation Systems are tools designed to help users to find items within a given domain, according to their own preferences expressed by means of a user profile. A general model for recommendation systems based on probabilistic graphical models is proposed in this paper. It is designed to deal with hierarchical domains, where the items can be grouped in a hierarchy, each item being only contained in another, more general item. The model makes decisions about which items in the hierarchy are more useful for the user, and carries out the necessary computations in a very efficient way.