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dc.contributor.authorLu, Yi-Shuen_US
dc.contributor.authorShih, Wen-Yuehen_US
dc.contributor.authorGau, Hung-Yien_US
dc.contributor.authorChung, Kuan-Chiehen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.date.accessioned2019-06-03T01:08:35Z-
dc.date.available2019-06-03T01:08:35Z-
dc.date.issued2019-05-01en_US
dc.identifier.issn1386-145Xen_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11280-018-0599-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/151954-
dc.description.abstractWith the increasing popularity of location-based social networks (LBSNs), users are able to share the Point-of-Interests (POIs) they visited by check-ins. By analyzing the users' historical check-in records, POI recommendation can help users get better visiting experience by recommending POIs which users may be interested in. Although recent successive POI recommendation methods consider geographical influence by measuring the distances among POIs, most of them ignore the influence of the regions where the POIs are located. Therefore, we propose in this paper two models to tackle the problem of successive POI recommendation. First, a feature-based successive POI recommendation method, named UGSE-LR, is proposed to take the influence of regions, named regional influence, into consideration when recommending POIs. UGSE-LR first splits an area into grids for estimating regional influence. Then, UGSE-LR applies Edge-weighted Personalized PageRank (EdgePPR) for modeling the successive transitions among POIs. Finally, UGSE-LR fuses user preference, regional influence and successive transition influence into a unified recommendation framework. In addition, with the aid of Recurrent Neural Network (RNN), we propose a latent-factor based successive POI recommendation method, named PEU-RNN, to integrate the sequential visits of POIs and user preference to recommend POIs. First, PEU-RNN adopts the word embedding technique to transform each POI into a latent vector. Then, RNN is used to recommend the POIs depend on the users' historical check-in records. Experimental results on two real LBSN datasets show that our methods are more accurate than the state-of-the-art successive POI recommendation methods in terms of precision and recall. In addition, experimental results also show that PEU-RNN is suitable for the datasets with many check-in records, while UGSE-LR is suitable for the datasets with moderate check-in records.en_US
dc.language.isoen_USen_US
dc.subjectSuccessive POI recommendationen_US
dc.subjectRecommendationen_US
dc.subjectLocation-based social networken_US
dc.titleOn successive point-of-interest recommendationen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11280-018-0599-5en_US
dc.identifier.journalWORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMSen_US
dc.citation.volume22en_US
dc.citation.issue3en_US
dc.citation.spage1151en_US
dc.citation.epage1173en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000466989400012en_US
dc.citation.woscount0en_US
Appears in Collections:Articles