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dc.contributor.authorLu, Yi-Shuen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.date.accessioned2020-01-02T00:04:18Z-
dc.date.available2020-01-02T00:04:18Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn0167-739Xen_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.future.2019.07.074en_US
dc.identifier.urihttp://hdl.handle.net/11536/153360-
dc.description.abstractPoint-of-Interest (POI) recommendation is an important service in location-based social networks (LBSNs) since it can help a user to discover new POIs for future visiting. In order to provide better recommendation experience, a novel POI recommendation paradigm, named successive POI recommendation, has been proposed. The difference between traditional POI recommendation and successive POI recommendation is that successive POI recommendation focuses on recommending POIs that the target user may like to visit within a time period (e.g., a few hours). To address this problem, we propose a new graph-based latent representation model called GLR, to obtain the latent vectors of temporal successive transition influence and temporal user preference based on the historical check-in records. We also propose a novel method named GLR_GT to employ these latent vectors and geographical influence of POIs to perform successive POI recommendation. Moreover, we also propose an extended method named GLR_GT_LSTM to employ a long short-term memory (LSTM) neural network to model users' complex transition behavior. Several experiments are conducted on some real-world LBSN datasets. Experimental results show that our proposed method GLR_GT_LSTM outperforms the other prior successive POI recommendation methods in terms of precision and recall. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectSuccessive POI recommendationen_US
dc.subjectUser preferenceen_US
dc.subjectSuccessive transition influenceen_US
dc.subjectGeographical influenceen_US
dc.subjectTemporal influenceen_US
dc.subjectRegional influenceen_US
dc.titleGLR: A graph-based latent representation model for successive POI recommendationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.future.2019.07.074en_US
dc.identifier.journalFUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCEen_US
dc.citation.volume102en_US
dc.citation.spage230en_US
dc.citation.epage244en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000501936300020en_US
dc.citation.woscount0en_US
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