Top-N Recommender System via Matrix Completion
This work addresses recommendation accuracy for users and platforms, but it appears incremental as it builds on existing matrix completion methods.
The paper tackles the problem of low recommendation quality in Top-N recommender systems by proposing a matrix completion algorithm that uses a nonconvex rank relaxation and efficient optimization, achieving new accuracy levels on real datasets.
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.