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Title: Location familiarity based flickr photographer classification for POI mining
Authors: Zhuang, Chenyi
Ma, Qiang  kyouindb  KAKEN_id  orcid (unconfirmed)
Yoshikawa, Masatoshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 馬, 強
吉川, 正俊
Keywords: User profiling
Location familiarity
Geo-tagged image
Probabilistic model
Social network
Issue Date: 3-Nov-2015
Publisher: Association for Computing Machinery, Inc. (ACM)
Journal title: GIS '15: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Thesis number: 84
Abstract: In this paper, we propose and compare three ways of modeling photographers' location familiarity: a social network driven model, a time driven model and a location driven model. Then, the integration of the three models is further discussed. Experimental evaluations and analysis on a real data set consisting of 14, 112 images collected from three cities well demonstrate the performance of the proposed classification methods. Many applications could benefit from information about the location familiarity, such as personalized geo-social recommendation, epidemic dispersion, urban computing, and so on.
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 "GIS '15, Article No. 84",
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1145/2820783.2820875
Appears in Collections:Journal Articles

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