融合用户信任度的概率矩阵分解推荐算法  

Probabilistic matrix factorization recommendation algorithm based on user trust

作　　者：陈辉[1] 王锴钺 CHEN Hui;WANG Kaiyue(College of Computer Science and Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《山东理工大学学报（自然科学版）》2021年第3期10-16,共7页Journal of Shandong University of Technology:Natural Science Edition

基　　金：国家自然科学基金项目(61170060);安徽省自然科学基金项目(1608085ME122);安徽省省级质量工程项目(2016tszy029)。

摘　　要：针对传统协同过滤推荐方法中用户评分信息稀疏导致推荐准确度不高的问题,提出融合用户信任度的概率矩阵分解推荐算法。该算法综合用户间的联合评分项以及非联合评分项,利用KL散度得到用户信任度排名,使得相似用户间的特征向量更加接近,并在概率矩阵分解过程中维持这种关系。最后在MovieLens 1M和Epinions数据集上采用三种指标进行对比实验,验证了该算法在稀疏数据集上推荐效果的有效性。In order to solve the problems that sparse user rating information density leads to low recommendation accuracy in the traditional collaborative filtering recommendation method,a probabilistic matrix factorization recommendation algorithm based on user trust is proposed.The algorithm first integrates the joint rating items and non-joint rating items between users,uses KL-divergence to obtain ranking of user trust which makes the feature vectors of similar users closer,and maintains this relationship during the probability matrix factorization process.Finally,a comparative experiment was conducted on the MovieLens 1M and Epinions datasets according to three indicators,which verified the effectiveness of the algorithm's recommendation effect on sparse datasets.

关 键 词：推荐算法 概率矩阵分解 KL散度 用户信任度 协同过滤 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]