Slope One algorithm based on nonnegative matrix factorization

被引：2

作者：

Dong L.-Y. ^{[1
,2
]}

Jin J.-H. ^{[1
]}

Fang Y.-C. ^{[1
]}

Wang Y.-Q. ^{[1
]}

Li Y.-L. ^{[3
]}

Sun M.-H. ^{[1
,2
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun

[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun

[3] School of Information Science and Technology, Northeast Normal University, Changchun

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2019年 / 53卷 / 07期

关键词：

Collaborative filtering; Non-negative matrix factorization; Recommendation system; Slope One;

D O I：

10.3785/j.issn.1008-973X.2019.07.014

中图分类号：

学科分类号：

摘要：

The good performance of matrix decomposition in solving matrix sparsity was used in order to solve the problem that the Slope One algorithm has low recommendation accuracy in the sparse data set in the collaborative filtering recommendation algorithm. The nonnegative matrix factorization technology was introduced into the dimension reduction of the user-item rating matrix in order to optimize the Slope One algorithm. The original sparse scoring matrix was non-negatively decomposed in order to improve the sparsity of the matrix. The experimental results show that the NMF-Slope One algorithm has a good recommendation effect compared with the original CF algorithm. Parameters were determined for experimentation under conditions of sparse data. The proposed method improves the accuracy and the recommendation quality of the Slope One algorithm under data sparseness. © 2019, Zhejiang University Press. All right reserved.

引用

页码：1349 / 1353and1362

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