Cross-Domain Matrix Factorization for Multiple Implicit-Feedback Domains

Cross-domain recommender systems represent an emerging research topic as users generally have interactions with items from multiple domains. One goal of a cross-domain recommender system is to improve the quality of recommendations in a target domain by using user preference information from other source domains. We observe that, in many applications, users interact with items of different types (e.g., artists and tags). Each recommendation problem, for example, recommending artists or recommending tags, can be seen as a different task, or, in general, a different domain. Furthermore, for such applications, explicit feedback may not be available, while implicit feedback is readily available. To handle such applications, in this paper, we propose a novel cross-domain collaborative filtering approach, based on a regularized latent factor model, to transfer knowledge between source and target domains with implicit feedback. More specifically, we identify latent user and item factors in the source domains, and transfer the user factors to the target, while controlling the amount of knowledge transferred through regularization parameters. Experimental results on six target recommendation tasks (or domains) from two real-world applications show the effectiveness of our approach in improving target recommendation accuracy as compared to state-of-the-art single-domain collaborative filtering approaches. Furthermore, preliminary results also suggest that our approach can handle varying percentages of user overlap between source and target domains.