Cross-Domain Collaborative Recommendation by Transfer Learning of Heterogeneous Feedbacks

Abstract

With the rapid development of information society, the era of big data is coming. Various recommendation systems are developed to make recommendations by mining useful knowledge from massive data. The big data is often multi-source and heterogeneous, which challenges the recommendation seriously. Collaborative filtering is the widely used recommendation method, but the data sparseness is its major bottleneck. Transfer learning can overcome this problem by transferring the learned knowledge from the auxiliary data to the target data for cross-domain recommendation. Many traditional transfer learning models for cross-domain collaborative recommendation assume that multiple domains share a latent common rating pattern which may lead to the negative transfer, and only apply to the homogeneous feedbacks. To address such problems, we propose a new transfer learning model. We do the collective factorization to rating matrices of the target data and its auxiliary data to transfer the rating information among heterogeneous feedbacks, and get the initial latent factors of users and items, based on which we construct the similarity graphs. Further, we predict the missing ratings by the twin bridge transfer learning of latent factors and similarity graphs. Experiments show that our proposed model outperforms the state-of-the-art models for cross-domain recommendation.