Cross-domain recommendation with Multi-Auxiliary Domains via Consistent and Selective Cluster-Level Knowledge Transfer.

Recommender systems are software tools and techniques designed to provide users with useful items suggestions. For most existing recommender systems, data sparsity is a challenging and common problem. The cross-domain recommender systems can mitigate the sparsity problem of data in a target domain by introducing auxiliary domain with relatively dense data. However, there are three challenging issues in the existing cross-domain recommender systems: (1) Most cross-domain recommender systems are designed for a single auxiliary domain, which may lead to insufficient knowledge transfer. However, in practice, there may be multiple auxiliary domains related to the target domain that are available; (2) Due to the distribution divergence between the auxiliary and target domains, inconsistent knowledge may be transferred to the target domain; (3) Because of the diversity among domains, they do not necessarily share exactly the same rating pattern. Moreover, not all cluster-level knowledge of auxiliary domains is beneficial to the target domain, and some cluster-level knowledge may be harmful to the target domain. To deal with the above three issues, we propose a Cross-Domain Recommendation with Multi-Auxiliary Domains via Consistent and Selective Cluster-Level Knowledge Transfer, called MD-CSKT. First, rating matrices from multiple auxiliary domains are decomposed jointly with a single target rating matrix to transfer richer knowledge from multiple auxiliary domains. Secondly, the Maximum Mean Discrepancy (MMD) regularization constraint is incorporated into the joint decomposition to reduce the distribution discrepancy between each auxiliary domain and the target domain, so as to achieve consistent cluster-level knowledge transfer. Thirdly, selective cluster-level knowledge transfer is realized by adaptively selecting of cluster-level rating knowledge from multiple auxiliary domains in the joint matrix factorization. The experimental results on six real-world datasets in three categories demonstrate that the presented MD-CSKT method substantially outperforms nine comparison methods and achieves the improvement of recommendation accuracy in the target domain. Compared with the state-of-the-art comparison methods, the overall performance of the MD-CSKT method is improved by more than 25%.