  Collaborative filtering is an important technique for recommendation. Whereas
it has been repeatedly shown to be effective in previous work, its performance
remains unsatisfactory in many real-world applications, especially those where
the items or users are highly diverse. In this paper, we explore an
ensemble-based framework to enhance the capability of a recommender in handling
diverse data. Specifically, we formulate a probabilistic model which integrates
the items, the users, as well as the associations between them into a
generative process. On top of this formulation, we further derive a progressive
algorithm to construct an ensemble of collaborative filters. In each iteration,
a new filter is derived from re-weighted entries and incorporated into the
ensemble. It is noteworthy that while the algorithmic procedure of our
algorithm is apparently similar to boosting, it is derived from an essentially
different formulation and thus differs in several key technical aspects. We
tested the proposed method on three large datasets, and observed substantial
improvement over the state of the art, including L2Boost, an effective method
based on boosting.
