Coupled Low Rank Approximation for Collaborative Filtering in Social Networks

Recommending items and friends are equally important in social recommendation applications. Current works usually consider them as two independent tasks and address them separately. This paper presents a new method to collaboratively perform the two tasks, in which three types of sparse data, ratings, trust, and distrust, are complemented with each other by a coupled low-rank approximation model. The data sparsity problems, which greatly decline prediction quality in respective tasks, are well mitigated in this way. The recovery conditions regarding the low-rank approximation are provided. Empirical studies on real data also support our theoretical models and analysis. To the best of our knowledge, this is the first attempt to raise and address such a problem.