We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于信任扩展和列表级排序学习的服务推荐方法.
- Authors
方晨; 张恒巍; 张铭; 王晋东
- Abstract
In view of the problem of trust relationship in traditional trust-based service recommendation algorithm, and the inaccuracy of service recommendation list obtained by sorting the predicted QoS, a trust expansion and listwise learning-to-rank based service recommendation method (TELSR) was proposed. The probabilistic user similarity computation method was proposed after analyzing the importance of service sorting information, in order to further improve the accuracy of similarity computation. The trust expansion model was presented to solve the sparseness of trust relationship, and then the trusted neighbor set construction algorithm was proposed by combining with the user similarity. Based on the trusted neighbor set, the listwise learning-to-rank algorithm was proposed to train an optimal ranking model. Simulation experiments show that TELSR not only has high recommendation accuracy, but also can resist attacks from malicious users.
- Publication
Journal on Communication / Tongxin Xuebao, 2018, Vol 39, Issue 1, p147
- ISSN
1000-436X
- Publication type
Article
- DOI
10.11959/j.issn.1000-436x.2018007