Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model
published: Aug. 26, 2009, recorded: June 2009, views: 5031
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Cross-domain collaborative ﬁltering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative ﬁltering. We ﬁrst show that the relatedness across multiple rating matrices can be established by ﬁnding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to ﬁll the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative ﬁltering data sets to show that RMGM can outperform the individual models trained separately.
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