Top-N Recommender System via Matrix Completion

TL;DR

This paper introduces a novel matrix completion approach for Top-N recommender systems, utilizing nonconvex rank relaxation to improve recommendation accuracy, validated through extensive experiments on real datasets.

Contribution

The paper presents a new low-rank matrix completion algorithm with nonconvex relaxation, enhancing Top-N recommendation quality over existing methods.

Findings

Significant improvement in recommendation accuracy

Effective use of nonconvex rank relaxation

Validated on multiple real-world datasets

Abstract

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.