QoS-aware listwise collaborative ranking algorithm for service recommendation

被引：0

作者：

Cao J.-H. ^{[1
,2
]}

Kong F.-S. ^{[1
]}

Ran Y.-Z. ^{[2
]}

机构：

[1] College of Mechanical Science and Engineering, Jilin University, Changchun

[2] Center for Computer Fundamental Education, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2018年 / 48卷 / 01期

关键词：

Collaborative filtering; Computer application service recommendation; Learning to rank; Matrix factorization;

D O I：

10.13229/j.cnki.jdxbgxb20161273

中图分类号：

学科分类号：

摘要：

With the increasing number of candidate services that meet the same function on the Internet, service selection becomes more and more difficult, and service recommendation becomes the key issue that needs to be solved urgently. However, the traditional service QoS prediction based recommendation method pays less attention to the role of the service ranking to the recommendation list, which can not accurately reflect the user preference. To solve the above problems, this paper proposes a QoS ranking learning based service recommendation algorithm. It selects low computational complexity listwise loss function to optimize the matrix factorization model, and further improves the accuracy of QoS ranking by mining the neighbor information between users. Experiments on real datasets show that the proposed algorithm has good performance. © 2018, Editorial Board of Jilin University. All right reserved.

引用

页码：274 / 280

页数：6

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