计算机工程与应用2019，Vol.55Issue(2)：137-141,5.DOI:10.3778/j.issn.1002-8331.1709-0499
结合用户组群和隐性信任的概率矩阵分解推荐
Probabilistic Matrix Factorization Recommendation with User Group and Implicit Trust
席茜 1张凤琴 1李小青1
作者信息
- 1. 空军工程大学 信息与导航学院,西安 710077
- 折叠
摘要
Abstract
Research suggests that adding explicit social trust to social network recommendations significantly improves the predictive accuracy of the scoring, but it is difficult to get trust score of users in real life. Previously, some scholars have studied and proposed a trust measurement method to calculate and predict the interaction between users and trust score. In this paper, a method of social trust relationship extraction based on Hellinger distance is proposed, and the simi-larity calculation is carried out by describing the f-divergence of one side node in binary network. Then, a new probability matrix factorization algorithm(CH-PMF)based on user group and implicit social relation is proposed by adding the hid-den information to the improved probability matrix. Experimental results show that the proposed model has almost the same performance as the actual result of the actual trust score expressed by users, and CH-PMF has a better recommenda-tion than other traditional algorithms when the trust data can not be extracted.关键词
社会网络/推荐系统/概率矩阵分解/信任关系Key words
social network/recommendation system/probability matrix factorization/trust relationship分类
信息技术与安全科学引用本文复制引用
席茜,张凤琴,李小青..结合用户组群和隐性信任的概率矩阵分解推荐[J].计算机工程与应用,2019,55(2):137-141,5.
