标题:A Novel Matrix Factorization Recommendation Algorithm Fusing Social Trust and Behaviors in Micro-Blogs
作者:Xu, Xiushan; Yuan, Dongfeng
作者机构:[Xu, Xiushan; Yuan, Dongfeng] Shandong Univ, Sch Informat Sci & Engn, Shandong Prov Key Lab Wireless Commun Technol, Jinan, Shandong, Peoples R China.
会议名称:2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)
会议日期:APR 28-30, 2017
来源:2017 2ND IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2017)
出版年:2017
页码:283-287
DOI:10.1109/ICCCBDA.2017.7951925
关键词:recommender systems; matrix factorization; explicit trust; implicit; trust
摘要:With the advent of social networks, micro-blogs have become increasingly popular and recommender systems have been widely used to provide personalized services for better user experience. Traditional collaborative filtering is one of the most popular approaches but it suffers with two well-known problems: cold start and data sparsity. Trust relationships and interaction behaviors in social networks can be used to find user's potential preferences. In this paper, we focus on the problem of followee recommendation in microblogs and we propose TBSVD, a social Trust and Behavior based Singular Value Decomposition algorithm. First, implicit trust is calculated based on user interaction behaviors including mention, comment and retweet while explicit trust is based on the direct connections between users; Then an extended trust matrix is constructed combining both implicit trust and explicit trust. Finally, we utilize both the extended trust and ratings and apply matrix factorization techniques to build the model. Experiments on KDD Cup 2012 dataset demonstrates that our approach TBSVD achieves better performance in terms of RMSE and MSE.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:1
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024397727&doi=10.1109%2fICCCBDA.2017.7951925&partnerID=40&md5=f162f3cb26ddecf0340d74b4a5c30727
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