Improved Bounded Matrix Completion for Large-Scale Recommender Systems

Huang Fang, Zhang Zhen, Yiqun Shao, Cho-Jui Hsieh
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Matrix completion is a widely used technique for personalized recommender system. In this paper, we focus on the idea of Bounded Matrix Completion (BMC) which imposes bounded constraint into the original matrix completion problem. It has been shown that BMC works well for several real world datasets, and an efficient coordinate descent solver called BMA has been proposed in~\cite{bma}. However, we observe that the BMA algorithm sometimes fails to converge to a stationary point, resulting in a
more » ... t, resulting in a relatively poor accuracy in those cases. To overcome this issue, we propose our new approach for solving BMC under the ADMM framework. The proposed algorithm is gauranteed to converge to stationary points. Experimental results on real world datasets show that our algorithm can reach a lower objective value, obtain a higher predict accuracy rate and have better scalability compared with BMA. We also present that our method outperforms the state-of-art standard matrix factorization in most cases.
doi:10.24963/ijcai.2017/229 dblp:conf/ijcai/FangZSH17 fatcat:7i6fqvylgrbpfnwvclr4qx6vdi