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dc.contributor.authorWang, Weiqing
dc.contributor.authorYin, Hongzhi
dc.contributor.authorDu, Xingzhong
dc.contributor.authorQuoc, Viet Hung Nguyen
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2019-07-02T12:31:12Z
dc.date.available2019-07-02T12:31:12Z
dc.date.issued2018
dc.identifier.issn2157-6904
dc.identifier.doi10.1145/3230706
dc.identifier.urihttp://hdl.handle.net/10072/384152
dc.description.abstractWith the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern; and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherASSOC COMPUTING MACHINERY
dc.relation.ispartofissue6
dc.relation.ispartofjournalACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
dc.relation.ispartofvolume9
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchDatabase Management
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode080604
dc.subject.fieldofresearchcode0801
dc.titleTPM: A Temporal Personalized Model for Spatial Item Recommendation
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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