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dc.contributor.authorTao, Ye
dc.contributor.authorWang, Can
dc.contributor.authorYao, Lina
dc.contributor.authorLi, Weimin
dc.contributor.authorYu, Yonghong
dc.date.accessioned2020-11-13T06:23:30Z
dc.date.available2020-11-13T06:23:30Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-6926-2
dc.identifier.doi10.1109/ijcnn48605.2020.9207708
dc.identifier.urihttp://hdl.handle.net/10072/399282
dc.description.abstractRecommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
dc.description.peerreviewedYes
dc.description.sponsorshipGriffith University
dc.publisherIEEE
dc.relation.ispartofconferencename2020 International Joint Conference on Neural Networks (IJCNN 2020)
dc.relation.ispartofconferencetitle2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofdatefrom2020-07-19
dc.relation.ispartofdateto2020-07-24
dc.relation.ispartoflocationGlasgow, United Kingdom
dc.subject.fieldofresearchNeural networks
dc.subject.fieldofresearchcode461104
dc.titleTRec: Sequential Recommender Based On Latent Item Trend Information
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationTao, Y; Wang, C; Yao, L; Li, W; Yu, Y, TRec: Sequential Recommender Based On Latent Item Trend Information, 2020 International Joint Conference on Neural Networks (IJCNN), 2020
dc.date.updated2020-11-13T06:21:03Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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gro.griffith.authorWang, Can


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