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dc.contributor.authorZhuang, Chenyien
dc.contributor.authorMa, Qiangen
dc.contributor.authorLiang, Xuefengen
dc.contributor.authorYoshikawa, Masatoshien
dc.contributor.alternative馬, 強ja
dc.contributor.alternative梁, 雪峰ja
dc.contributor.alternative吉川, 正俊ja
dc.date.accessioned2016-12-14T02:16:32Z-
dc.date.available2016-12-14T02:16:32Z-
dc.date.issued2015-08-25-
dc.identifier.isbn9781450338547-
dc.identifier.urihttp://hdl.handle.net/2433/217604-
dc.description.abstractIn contrast to conventional studies of discovering hot spots, by analyzing geo-tagged images on Flickr, we introduce novel methods to discover obscure sightseeing spots that are less well-known while still worth visiting. To this end, we face two new challenges that the classical authority analysis based methods do not encounter: how to discover and rank spots on the basis of 1) popularity (obscurity level) and 2) scenery quality. For the first challenge, we estimate the obscurity level of a spot in accordance with the visiting asymmetry between photographers who are familiar with a target city and those who are not. For the second challenge, the behavior of both viewers who browsed the images and photographers are analyzed per each spot. We also develop an application system to help users to explore sightseeing spots with different geographical granularities. Experimental evaluations and analysis on a real dataset well demonstrate the effectiveness of the proposed methods.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherAssociation for Computing Machinery, Inc. (ACM)en
dc.rights© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in "ASONAM '15, Pages 590-595", http://dx.doi.org/10.1145/2808797.2809386.en
dc.rightsこの論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。ja
dc.rightsThis is not the published version. Please cite only the published version.en
dc.titleDiscovering obscure sightseeing spots by analysis of geo-tagged social imagesen
dc.typeconference paper-
dc.type.niitypeConference Paper-
dc.identifier.jtitleASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015en
dc.identifier.spage590-
dc.identifier.epage595-
dc.relation.doi10.1145/2808797.2809386-
dc.textversionauthor-
dcterms.accessRightsopen access-
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