Abstract
The problem of building Recommender Systems has attracted considerable attention in recent years, but most recommender systems are designed for recommending items for individuals. The aim of this paper is to automatically recommend and rank a list of new items to a group of users. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where the group's rates are computed from past voting patterns of other users with similar tastes. The use of Bayesian networks allows us to obtain an intuitive representation of the mechanisms that govern the relationships between the group members.