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Journal of Software:2018.29(12):3747-3763

基于信任机制下概率矩阵分解的用户评分预测
杜东舫,徐童,鲁亚男,管楚,刘淇,陈恩红
(大数据分析与应用安徽省重点实验室(中国科学技术大学), 安徽 合肥 230027;中国科学技术大学 计算机科学与技术学院, 安徽 合肥 230027)
User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization
DU Dong-Fang,XU Tong,LU Ya-Nan,GUAN Chu,LIU Qi,CHEN En-Hong
(Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China;School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China)
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Received:September 08, 2016    Revised:December 20, 2016
> 中文摘要: 互联网的蓬勃发展,在为用户提供便利的同时,其海量信息也为用户选择造成了困难,基于用户理解的信息推荐服务正成为应时之需.相较于面向单个用户信息的传统推荐技术,基于社交信息的推荐技术通过引入影响力建模,可以更真实地还原用户属性及行为.然而,已有的社交推荐技术往往停留于对用户影响的笼统归纳,并没有对其内在机制进行清晰分类和量化.针对这一问题,通过对用户评分行为中的信任关系进行分析,着重研究了信任用户间接影响用户偏好和直接影响用户评分两种不同机制,进而提出了基于用户间信任关系融合建模的概率矩阵分解模型TPMF,从而实现对上述两种机制的有效融合.在此基础之上,针对不同用户受两种机制影响权重不同的问题,通过借助评分相关性对用户进行聚类并映射到相应权重,实现了用户模型参数的个性化选择.公开数据集的多项实验结果表明:提出的TPMF及其衍生算法在各项指标上优于现有代表性算法,验证了所提出的影响机制及技术框架的有效性.
Abstract:The development of Internet has brought convenience to the public, but also troubles users in making choices among enormous data. Thus, recommender systems based on user understanding are urgently in need. Different from the traditional techniques that usually focus on individual users, the social-based recommender systems perform better with integrating social influence modeling to achieve more accurate user profiling. However, current works usually generalize influence in simple mode, while deep discussions on intrinsic mechanism have been largely ignored. To solve this problem, this paper studies the social influence within users who affects both rating and user attributes, and then proposes a novel trust-driven PMF (TPMF) algorithm to merge these two mechanisms. Furthermore, to deal with the task that different user should have personalized parameters, the study clusters users according to rating correlation and then maps them to corresponding weights, thereby achieving the personalized selection of users' model parameters. Comprehensive experiments on open data sets validate that TPMF and its derivation algorithm can effectively predict users' rating compared with several state of the art baselines, which demonstrates the capability of the presented influence mechanism and technical framework.
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基金项目:国家杰出青年科学基金(61325010);国家自然科学基金(U1605251,61703386,61403358);安徽省自然科学基金(1708085QF140);中央高校基本科研业务费专项资金(WK2150110006) 国家杰出青年科学基金(61325010);国家自然科学基金(U1605251,61703386,61403358);安徽省自然科学基金(1708085QF140);中央高校基本科研业务费专项资金(WK2150110006)
Foundation items:National Natural Science Funds for Distinguished Young Scholar (61325010); National Natural ScienceFoundation of China (U1605251, 61703386, 61403358); Anhui Natural Science Foundation (1708085QF140); Fundamental ResearchFunds for the Central Universities (WK2150110006)
Author NameAffiliationE-mail
DU Dong-Fang Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
 
XU Tong Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
tongxu@ustc.edu.cn 
LU Ya-Nan Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
 
GUAN Chu Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
 
LIU Qi Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
 
CHEN En-Hong Anhui Province Key Laboratory of Big Data Analysis and Application(University of Science and Technology of China), Hefei 230027, China
School of Computer Science and Technology, University of Science and Technology of China), Hefei 230027, China 
 
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杜东舫,徐童,鲁亚男,管楚,刘淇,陈恩红.基于信任机制下概率矩阵分解的用户评分预测.软件学报,2018,29(12):3747-3763

DU Dong-Fang,XU Tong,LU Ya-Nan,GUAN Chu,LIU Qi,CHEN En-Hong.User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization.Journal of Software,2018,29(12):3747-3763