标题:Listwise Collaborative Filtering
作者:Huang, Shanshan; Wang, Shuaiqiang; Liu, Tie-Yan; Ma, Jun; Chen, Zhumin; Veijalainen, Jari
作者机构:[Huang, Shanshan; Ma, Jun; Chen, Zhumin] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China.; [Wang, Shuaiqiang; Veijalainen, Jari] Univ 更多
会议名称:38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
会议日期:AUG 09-13, 2015
来源:SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
出版年:2015
页码:343-352
DOI:10.1145/2766462.2767693
关键词:Recommender systems; Collaborative filtering; Ranking oriented; collaborative filtering
摘要:Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items. One important advantage of ListCF lies in its ability of reducing the computational complexity of the training and prediction procedures while achieving the same or better ranking performances as compared to previous ranking-oriented memory-based CF algorithms. Extensive experiments on three benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:9
Scopus被引频次:16
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-s2.0-84953725382&doi=10.1145%2f2766462.2767693&partnerID=40&md5=bee3104755fe6060d2aa5bbdfb855f1e
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