标题:An implicit feedback integrated LDA-based topic model for IPTV program recommendation
作者:Zhang, Jie ;Li, Yujun ;Chen, Mo ;You, Lina
通讯作者:Li, Yujun
作者机构:[Zhang, Jie ;Li, Yujun ;Chen, Mo ;You, Lina ] School of Information Science and Engineering, Shandong University, China
会议名称:16th International Symposium on Communications and Information Technologies, ISCIT 2016
会议日期:26 September 2016 through 28 September 2016
来源:2016 16th International Symposium on Communications and Information Technologies, ISCIT 2016
出版年:2016
页码:216-220
DOI:10.1109/ISCIT.2016.7751624
关键词:Implicit feedback; IPTV program recommendation; Latent Dirichlet allocation (LDA)
摘要:Internet protocol television (IPTV), the television services through the Internet, has become more and more popular in recent years. Many recommendation systems have been made for delivering personalized IPTV services, of which understanding users' preference is the most critical. The traditional recommendation system only considers the users' playing behavior, but other implicit feedback behaviors of users, such as browsing, collecting also reflect the users' preference. We propose a novel latent Dirichlet allocation (LDA)-based model, which considers users' playing behavior as well as the implicit feedback of browsing and collecting, to capture the inherent viewing preference of individual users. The implicit feedback integrated LDA model employs three LDA models (the playing, browsing, and collecting behavior topic model), which are integrated via TV program characteristic. Based on this, we further calculate the ratio of each behavior to the recommended results by logistic regression algorithm. The experimental results show that the proposed topic model yields an average 32.5% precision for recommending 10 videos and 200 topics in IPTV recommendation, and its performance is an average of 19.5% higher than that of LDA using playing behavior only. © 2016 IEEE.
收录类别:EI;SCOPUS
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-s2.0-85006877925&doi=10.1109%2fISCIT.2016.7751624&partnerID=40&md5=77ddfdeff6ebbd90d50de8c0035e3682
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