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dc.contributor.authorGuo, Lei
dc.contributor.authorYin, Hongzhi
dc.contributor.authorWang, Qinyong
dc.contributor.authorChen, Tong
dc.contributor.authorZhou, Alexander
dc.contributor.authorNguyen, Quoc Viet Hung
dc.date.accessioned2020-03-19T04:57:52Z
dc.date.available2020-03-19T04:57:52Z
dc.date.issued2019
dc.identifier.isbn9781450362016
dc.identifier.doi10.1145/3292500.3330839
dc.identifier.urihttp://hdl.handle.net/10072/392471
dc.description.abstractSession-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. Recent studies address (1) by exploiting the attention mechanism in Recurrent Neural Network (RNN) to better model the user's current intent, which leads to promising improvements. However, the proposed attention models are based solely on the current session. Moreover, existing studies only perform SR under static offline settings and none of them explore (2). In this work, we target SSR and propose a Streaming Session-based Recommendation Machine (SSRM) to tackle these two challenges. Specifically, to better understand the uncertainty of user behaviors, we propose a Matrix Factorization (MF) based attention model, which improves the commonly used attention mechanism by leveraging the user's historical interactions. To deal with the large-volume and high-velocity challenge, we introduce a reservoir-based streaming model where an active sampling strategy is proposed to improve the efficiency of model updating. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of the SSRM method compared to several state-of-the-art methods in terms of MRR and Recall.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofconferencename25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019)
dc.relation.ispartofconferencetitleKDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
dc.relation.ispartofdatefrom2019-08-04
dc.relation.ispartofdateto2019-08-08
dc.relation.ispartoflocationAnchorage, USA
dc.relation.ispartofpagefrom1569
dc.relation.ispartofpageto1577
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode0806
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Information Systems
dc.subject.keywordsComputer Science, Theory & Methods
dc.titleStreaming Session-based Recommendation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationGuo, L; Yin, H; Wang, Q; Chen, T; Zhou, A; Nguyen, QVH, Streaming Session-based Recommendation, KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1569-1577
dc.date.updated2020-03-19T04:56:49Z
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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