Local Ensemble across Multiple Sources for Collaborative Filtering

Recently, Transfer Collaborative Filtering (TCF) methods across multiple source domains, which employ knowledge from different source domains to improve the recommendation performance in the target domain, have been applied in recommender systems. The existing multi-source TCF methods either require overlapping objects in different domains or simply re-weight domains to merge them together. In this paper, we propose a novel LO cal EN semble framework across multiple source domains for collaborative filtering (called LOEN for short), where weights of multiple sources for each missing rating in the target domain are determined according to their corresponding local structures. Compared with the previous TCF methods, LOEN does not require overlapping data and considers the divergence of sources through exploiting the local structures of ratings, which allows LOEN to be more general and effective. Experiments conducted on real datasets validate the effectiveness of LOEN, especially for knowledge transfer across unrelated source domains.