Matrix completion has become a popular method for top-N recommendation due to the low rank nature of sparse rating matrices. However, many existing methods produce top-N recommendations by recovering a user-item matrix solely based on a low rank function or its relaxations, while ignoring other important intrinsic characteristics of the top-N recommendation tasks such as preference ranking over the items. In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation. The proposed method is formulated as a joint minimization problem and solved using an ADMM algorithm. We conduct experiments on E-commerce datasets. The experimental results show the proposed approach outperforms several state-of-the-art methods.

27th International Joint Conference on Artificial Intelligence, IJCAI 2018
School of Computer Science

Wang, Z. (Zengmao), Guo, Y, & Du, B. (Bo). (2018). Matrix completion with preference ranking for top-n recommendation. In IJCAI International Joint Conference on Artificial Intelligence (pp. 3585–3591).