rank approximation 说的是矩阵的秩估计,而不是排序指标估计
解决问题的思路和动机,ref作者的另一篇文章;http://nuoku.vip/users/2/articles/83
唯一的区别在于: 构建rank approximation方法不同.
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Top-N Recommendation with Novel Rank Approximation | |||||||||||
| 文献作者 | Zhao Kang Qiang Cheng | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| 非凸秩估计; slim | |||||||||||
| 摘要描述 | |||||||||||
| The importance of accurate recommender systems has been widely recognized by academia and industry. How- ever, 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 nu- clear 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 con- vex 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. | |||||||||||
