MINDTL: Multiple Incomplete Domains Transfer Learning for Information Recommendation

摘要

Collaborative filtering is the most popular and successful information recom-mendation technique. However, it can suffer from data sparsity issue in cases where the systems do not have sufficient domain infor-mation. Transfer learning, which enables infor-mation to be transferred from source domains to target domain, presents an unprecedented opportunity to alleviate this issue. A few re-cent works focus on transferring user-item rating information from a dense domain to a sparse target domain, while almost all meth-ods need that each rating matrix in source domain to be extracted should be complete. To address this issue, in this paper we propose a novel multiple incomplete domains transfer learning model for cross-domain collaborative filtering. The transfer learning process con-sists of two steps. First, the user-item ratings information in incomplete source domains are compressed into multiple informative compact cluster-level matrixes, which are referred as codebooks. Second, we reconstruct the target matrix based on the codebooks. Specifically, for the purpose of maximizing the knowledge transfer, we design a new algorithm to learn the rating knowledge efficiently from multiple incomplete domains. Extensive experiments on real datasets demonstrate that our proposed approach significantly outperforms existing methods.