AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Top-N Recommender System via Matrix Completion
Zhao Kang, Chong Peng, Qiang Cheng

Last modified: 2016-02-21


Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.


Top-N recommender system; matrix completione; nonconvex rank relaxation; log-determinant; nuclear norm

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