Top-N Recommendation with Novel Rank Approximation
This work addresses the need for more accurate recommender systems in academia and industry, representing an incremental improvement over existing methods.
The paper tackles the problem of low recommendation quality in Top-N recommender systems by proposing a novel rank approximation with controllable error, which substantially improves accuracy on real data.
The importance of accurate recommender systems has been widely recognized by academia and industry. However, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approach uses the nuclear norm as a convex relaxation for the rank function and has achieved better recommendation accuracy than the state-of-the-art methods. In the past several years, solving rank minimization problems by leveraging nonconvex relaxations has received increasing attention. Some empirical results demonstrate that it can provide a better approximation to original problems than convex relaxation. In this paper, we propose a novel rank approximation to enhance the performance of Top-N recommendation systems, where the approximation error is controllable. Experimental results on real data show that the proposed rank approximation improves the Top-$N$ recommendation accuracy substantially.