通信学报2018，Vol.39Issue(1)：147-158,12.DOI:10.11959/j.issn.1000-436x.2018007
基于信任扩展和列表级排序学习的服务推荐方法
Trust expansion and listwise learning-to-rank based service recommendation method
摘要
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.关键词
服务推荐/排序学习/概率型用户相似度/信任关系Key words
service recommendation/learning-to-rank/probabilistic user similarity/trust relationship分类
信息技术与安全科学引用本文复制引用
方晨,张恒巍,张铭,王晋东..基于信任扩展和列表级排序学习的服务推荐方法[J].通信学报,2018,39(1):147-158,12.基金项目
国家自然科学基金资助项目(No.61303074,No.61309013) （No.61303074,No.61309013）
河南省科技攻关计划基金资助项目(No.12210231003)The National Natural Science Foundation of China (No.61303074,No.61309013),Henan Science and Technology Research Project (No.12210231003) （No.12210231003）
