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