This paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being universally appreciated ) and local patterns (tendency of users within a community to express a common preference on the same group of items). The core of our approach is a probabilistic co-clustering strategy, arranged in a hierarchical fashion: first, user communities are discovered, and then the information provided by each user community is used to discover topics, grouping items into categories. The experimental evaluation shows that the proposed model achieves a competitive prediction accuracy with respect to the state-of-art collaborative filtering approaches.
