信息与控制2017，Vol.46Issue(4)：400-407,8.DOI:10.13976/j.cnki.xk.2017.0400
融合社会标签的联合概率矩阵分解推荐算法
Unified Probabilistic Matrix Factorization Recommendation Algorithm Fusing Social Tagging
摘要
Abstract
Traditional recommendation systems only use the users′ rating information for the calculation and the recommendation.We can obtain the latent feature of the users or the resources to some extent but cannot get enough semantic interpretation which affects recommendation results.In order to solve this problem, we propose a neighborhood-aware unified probabilistic matrix factorization recommendation algorithm which combines social tags.First, we calculate the similarity between the users and the resources through the similarity of the tags to make neighborhood selection.Second, we construct a user-resource rating matrix, a user-tag tagging matrix and a resources-tag correlation matrix, and use the unified probability matrix factorization to get the latent feature vectors of three matrices to recommend by optimizing training model parameter.The experimental results show that the proposed algorithm can effectively use the semantics of the tags and improve the recommendation quality.关键词
社会标签/近邻感知/联合概率矩阵分解/推荐算法Key words
social tagging/neighborhood-aware/unified probability matrix factorization/recommendation algorithm分类
信息技术与安全科学引用本文复制引用
曹玉琳,李文立,郑东霞..融合社会标签的联合概率矩阵分解推荐算法[J].信息与控制,2017,46(4):400-407,8.基金项目
国家自然科学基金重点资助项目(71431002) （71431002）
国家创新群体项目(71421001) （71421001）
辽宁省自然科学基金资助项目(2015020035) （2015020035）
辽宁省教育厅一般项目(71600136) （71600136）
