In this paper we propose a probabilistic co-clustering approach for pattern discovery in collaborative filtering data. We extend the Block Mixture Model in order to learn about the structures and relationships within preference data. The resulting model can simultaneously cluster users into communities and items into categories. Besides its predictive capabilities, the model enables the discovery of significant knowledge patterns, such as the analysis of common trends and relationships between items and users within communities/categories. We reformulate the mathematical model and implement a parameter estimation technique. Next, we show how the model parameters enable pattern discovery tasks, namely: (i) to infer topics for each items category and characteristic items for each user community; (ii) to model community interests and transitions among topics. Experiments on MovieLens data provide evidence about the effectiveness of the proposed approach.
