Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model--Extended Version
This addresses the sparsity problem in recommendation systems for users by transferring behavior patterns across domains, though it appears incremental as it builds on existing latent factor approaches.
The paper tackles the problem of cross-domain recommendation by proposing a Probabilistic Cluster-level Latent Factor (PCLF) model to capture domain diversities, and it demonstrates that this model outperforms state-of-the-art methods on real-world datasets.
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.