COLLABORATIVE PREDICTION BY MULTIPLE BAYESIAN NETWORKS AND ITS APPLICATION TO PRINTER USAGE MODELING

Bayesian networks (BNs) have become increasingly popular tools for learning and prediction in the context of user modeling. However, due to the lacks of individual training data especially for inactive or new users, separate treatments of individual users often become quite problematic. In this article, we propose a novel scheme of ‘collaborative’ prediction, in which multiple BNs (each for a single user) are cooperatively employed for prediction of a single target user’s actions. The core idea is to simply use the Bayesian principle, but in a rather intricate way. Our method is simple, and thus readily applicable to rather difficult, cases with heterogeneous and preliminary unknown structures of individual BNs. We demonstrate the usefulness of our method with a real dataset related to our motivating user modeling task, i.e., printer usage prediction. The results show that our Bayesian collaboration method effectively improves the prediction accuracy from both non-collaborative and other naive collaborative approaches, especially in cases with small sample sizes. It also appears that our method can improve such a low prediction accuracy that is possibly caused by poor optimization in the structure learning.