Heterogeneous knowledge transfer via domain regularization for improving cross-domain collaborative filtering
Yizhou Zang, Xiaohua Tony Hu · 2017
Cross-Domain Collaborative Filtering(CDCF) methods transfer knowledge from auxiliary domains (e.g., books) to improve recommendation in a target domain (e.g. movies). Most CDCF methods exploit homogeneous user feedback, e.g. numeric ratings, from auxiliary domains as the knowledge source. However, in a typical recommender system, the usage data is usually heterogeneous and therefore is potential to better improve recommendation in other domains. In this paper, we propose a novel and generic CDCF solution called Heterogeneous Knowledge Transfer via Domain Regularization (HKT-DR). Our solution is able to mine high quality knowledge from heterogeneous knowledge sources, i.e. both explicit and implicit feedbacks from multiple auxiliary domains, by building a fused user similarity network, and to incorporate the knowledge by imposing domain regularization to constrain matrix factorization objective function. Extensive experiments on real world datasets show that the proposed HKT-DR model outperforms the state-of-the-art CDCF solutions.