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Cross-domain recommendation has been proposed to transfer user behaviorpattern by pooling together the rating data from multiple domains to alleviatethe sparsity problem appearing in single rating domains. However, previousmodels only assume that multiple domains share a latent common rating patternbased on the user-item co-clustering. To capture diversities among differentdomains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF)model to improve the cross-domain recommendation performance. Experiments onseveral real world datasets demonstrate that our proposed model outperforms thestate-of-the-art methods for the cross-domain recommendation task.

Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model--Extended Version