一种基于信任机制的概率矩阵分解协同过滤推荐算法  

Probabilistic Matrix Factorization Algorithm of Collaborative Filtering Based on Trust Mechanism

作　　者：王建芳[1] 苗艳玲 韩鹏飞 刘永利[1] WANG Jian-fang;MIAO Yan-ling;HAN Peng-fei;LIU Yong-li(College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454000

出　　处：《小型微型计算机系统》2019年第1期31-35,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61202286)资助;河南省高等学校重点科研项目(15A520074)资助;河南省高等学校青年骨干教师项目(2015GGJS-068)资助

摘　　要：传统的概率矩阵分解算法虽然较好地解决了推荐过程中的稀疏性和用户冷启动问题,但由于没有考虑到用户之间的信任信息,造成推荐精度不高.本文利用用户评分过程中潜在存在的信任关系,提出一种基于信任机制的概率矩阵分解协同过滤推荐算法TM-PMF(Probabilistic Matrix Factorization Algorithm of Collaborative Filtering Based on Trust Mechanism).首先根据用户间的信任关系来构建信任网络以获得信任评分矩阵.然后将信任评分矩阵与用户评分矩阵进行融合构建用户-信任评分矩阵,接着通过概率矩阵分解技术获得最优推荐列表.最终实验结果表明在不同稀疏数据集上,本文提出的TM-PMF算法较传统算法在精度方面有较大幅度地提高.Although the Probabilistic Matrix Factorization algorithm is better to alleviate the data sparsity and cold start that every collaborative filtering algorithm or recommendation system confronts,due to ignore the trust information among users,the prediction accuracy is not high. In this paper,a probabilistic matrix factorization algorithm of collaborative filtering based on trust mechanism( TM-PMF) is proposed by employing both users’ potential trust relationship and rating records. First,the trust network is constructed according to the trust relationship,to obtain the trust score matrix. Second,the trust score matrix is merged with the user-item rating matrix to construct the user-trust matrix. Finally,the optimal recommendation list is obtained by the Probabilistic Matrix Factorization technique. Experimental results showthat the algorithm proposed in this paper is more accurate than the traditional algorithm in different sparse data sets,especially for the Probabilistic Matrix Factorization model.

关 键 词：协同过滤 数据稀疏 信任机制 信任值 概率矩阵分解 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]