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dc.contributor.authorSun, Ke
dc.contributor.authorQian, Tieyun
dc.contributor.authorChen, Tong
dc.contributor.authorLiang, Yile
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorYin, Hongzhi
dc.date.accessioned2020-11-13T04:37:01Z
dc.date.available2020-11-13T04:37:01Z
dc.date.issued2020
dc.identifier.isbn978-1-57735-835-0
dc.identifier.issn2159-5399
dc.identifier.doi10.1609/aaai.v34i01.5353
dc.identifier.urihttp://hdl.handle.net/10072/399266
dc.description.abstractPoint-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.
dc.description.peerreviewedYes
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI) Press
dc.relation.ispartofconferencenameThirty-Fourth AAAI Conference on Artificial Intelligence
dc.relation.ispartofconferencetitleProceedings of the AAAI Conference on Artificial Intelligence
dc.relation.ispartofdatefrom2020-02-07
dc.relation.ispartofdateto2020-02-12
dc.relation.ispartoflocationNew York, USA
dc.relation.ispartofpagefrom214
dc.relation.ispartofpageto221
dc.relation.ispartofissue01
dc.relation.ispartofvolume34
dc.subject.fieldofresearchPattern recognition
dc.subject.fieldofresearchData mining and knowledge discovery
dc.subject.fieldofresearchcode460308
dc.subject.fieldofresearchcode460502
dc.titleWhere to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationSun, K; Qian, T; Chen, T; Liang, Y; Nguyen, QVH; Yin, H, Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation, Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01), pp. 214-221
dc.date.updated2020-11-13T04:35:28Z
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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