Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model

Authors

  • Siting Ren Beijing University of Posts and Telecommunications
  • Sheng Gao PRIS - Beijing University of Posts and Telecommunications
  • Jianxin Liao Beijing University of Posts and Telecommunications
  • Jun Guo PRIS - Beijing University of Posts and Telecommunications

DOI:

https://doi.org/10.1609/aaai.v29i1.9706

Abstract

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.

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Published

2015-03-04

How to Cite

Ren, S., Gao, S., Liao, J., & Guo, J. (2015). Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9706