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Title: Discovering obscure sightseeing spots by analysis of geo-tagged social images
Authors: Zhuang, Chenyi
Ma, Qiang  kyouindb  KAKEN_id
Liang, Xuefeng
Yoshikawa, Masatoshi  kyouindb  KAKEN_id
Author's alias: 馬, 強
梁, 雪峰
吉川, 正俊
Issue Date: 25-Aug-2015
Publisher: Association for Computing Machinery, Inc. (ACM)
Journal title: ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
Start page: 590
End page: 595
Abstract: In 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.
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.
This is not the published version. Please cite only the published version. 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。
URI: http://hdl.handle.net/2433/217604
DOI(Published Version): 10.1145/2808797.2809386
Appears in Collections:Journal Articles

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