标题:DPMFNeg: A Dynamically Integrated Model for Collaborative Filtering
作者:Yang, Wenlong; Ma, Jun; Huang, Shanshan; Yang, Tongfeng
作者机构:[Yang, Wenlong; Ma, Jun; Huang, Shanshan; Yang, Tongfeng] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.
会议名称:16th Asia-Pacific Web Conference (APWeb)
会议日期:SEP 05-07, 2014
来源:WEB TECHNOLOGIES AND APPLICATIONS, APWEB 2014
出版年:2014
卷:8709
页码:568-575
DOI:10.1007/978-3-319-11116-2_53
关键词:Collaborative filtering; Recommender Systems; Neighborhood Approach;; Probabilistic Matrix Factorization
摘要:Collaborative Filtering (CF) techniques are the mostly applied methods in real world recommender systems. There are two typical types of CF, which are memory-based and model-based CF algorithms. However, these two CF methods in fact pay attention to different parts of ratings data. Memory-based CF methods are adept at finding local similar users, while model-based CF algorithms emphasize achieving global optimization. In this paper, we integrate a neighborhood approach and Probabilistic Matrix Factorization (PMF) into a hybrid CF model, DPMFNeg, which combines the advantages of memory-based and model-based CF algorithms. We explore the performance of our method on two test datasets - MoiveLens-100K and MoiveLens-1M. The results show that DPMFNeg performs better than other methods on those datasets in terms of MAE and RMSE.
收录类别:CPCI-S;EI;SCOPUS
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-s2.0-84958548572&doi=10.1007%2f978-3-319-11116-2_53&partnerID=40&md5=961a97e90adaa3d107f3e05aca3962c4
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