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dc.contributor.authorWang, Weiqing
dc.contributor.authorYin, Hongzhi
dc.contributor.authorHuang, Zi
dc.contributor.authorWang, Qinyong
dc.contributor.authorDu, Xingzhong
dc.contributor.authorQuoc, Viet Hung Nguyen
dc.date.accessioned2019-05-29T12:44:11Z
dc.date.available2019-05-29T12:44:11Z
dc.date.issued2018
dc.identifier.isbn9781450356572
dc.identifier.doi10.1145/3209978.3210016
dc.identifier.urihttp://hdl.handle.net/10072/379949
dc.description.abstractStudying recommender systems under streaming scenarios has become increasingly important because real-world applications produce data continuously and rapidly. However, most existing recommender systems today are designed in the context of an offline setting. Compared with the traditional recommender systems, large-volume and high-velocity are posing severe challenges for streaming recommender systems. In this paper, we investigate the problem of streaming recommendations being subject to higher input rates than they can immediately process with their available system resources (i.e., CPU and memory). In particular, we provide a principled framework called as SPMF (Stream-centered Probabilistic Matrix Factorization model), based on BPR (Bayesian Personalized Ranking) optimization framework, for performing efficient ranking based recommendations in stream settings. Experiments on three real-world datasets illustrate the superiority of SPMF in online recommendations.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeUnited States
dc.relation.ispartofchapter42295
dc.relation.ispartofconferencename41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
dc.relation.ispartofconferencetitleACM/SIGIR PROCEEDINGS 2018
dc.relation.ispartofdatefrom2018-07-08
dc.relation.ispartofdateto2018-07-12
dc.relation.ispartoflocationUniv Michigan, Ann Arbor, MI
dc.relation.ispartofpagefrom525
dc.relation.ispartofpageto534
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchcode460505
dc.titleStreaming Ranking Based Recommender Systems
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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