Transfer learning for collaborative filtering via a rating-matrix generative model

Bin Li, Qiang Yang, Xiangyang Xue
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
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
more » ... ewed 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.
doi:10.1145/1553374.1553454 dblp:conf/icml/LiYX09 fatcat:jjevdbkewvc3zeilcsv35irihy