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dc.contributor.authorHashim-Jones, Jake
dc.contributor.authorWang, Can
dc.contributor.authorIslam, Md Saiful
dc.contributor.authorStantic, Bela
dc.contributor.editorWang, J
dc.contributor.editorCong, G
dc.contributor.editorChen, J
dc.contributor.editorQi, J
dc.date.accessioned2020-03-26T04:39:20Z
dc.date.available2020-03-26T04:39:20Z
dc.date.issued2018
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-92013-9_13
dc.identifier.urihttp://hdl.handle.net/10072/376147
dc.description.abstractPoint-of-Interest (POI) recommendation is an important way to help people discover attractive places. POI recommendation approaches are usually based on collaborative filtering methods, whose performances are largely limited by the extreme scarcity of POI check-ins and a lack of rich contexts, and also by assuming the independence of locations. Recent strategies have been proposed to capture the relationship between locations based on statistical analysis, thereby estimating the similarity between locations purely based on the visiting frequencies of multiple users. However, implicit interactions with other link locations are overlooked, which leads to the discovery of incomplete information. This paper proposes a interdependent item-based model for POI recommender systems, which considers both the intra-similarity (i.e. co-occurrence of locations) and inter-similarity (i.e. dependency of locations via links) between locations, based on the TF-IDF conversion of check-in times. Geographic information, such as the longitude and latitude of locations, are incorporated into the interdependent model. Substantial experiments on three social network data sets verify the POI recommendation built with our proposed interdependent model achieves a significant performance improvement compared to the state-of-the-art techniques.
dc.description.peerreviewedYes
dc.description.sponsorshipGriffith University
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofpagefrom161
dc.relation.ispartofpageto173
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume10837
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode080109
dc.subject.fieldofresearchcode0801
dc.titleInterdependent Model for Point-of-Interest Recommendation via Social Networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© Springer International Publishing AG, part of Springer Nature 2018. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela
gro.griffith.authorHashim-Jones, Jake D.
gro.griffith.authorIslam, Saiful
gro.griffith.authorWang, Can


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