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Title: SNS user classification and its application to obscure POI discovery
Authors: Zhuang, Chenyi
Ma, Qiang  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-3430-9244 (unconfirmed)
Yoshikawa, Masatoshi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-1176-700X (unconfirmed)
Author's alias: 馬, 強
吉川, 正俊
Keywords: User profiling
Obscure points of interests
Probabilistic model
Social network
Issue Date: Feb-2017
Publisher: Springer Nature
Journal title: Multimedia Tools and Applications
Volume: 76
Issue: 4
Start page: 5461
End page: 5487
Abstract: Technologies are increasingly taking advantage of the explosion of social media (e.g., web searches, ad targeting, personalized geo-social recommendations, urban computing). Estimating the characteristics of users, or user profiling, is one of the key challenges for such technologies. This paper focuses on the important problem of automatically estimating social networking service (SNS) user authority with a given city, which can significantly improve location-based services and systems. The “authority” in our work measures a user’s familiarity with a particular city. By analyzing users’ social, temporal, and spatial behavior, we respectively propose and compare three models for user authority: a social-network-driven model, time-driven model, and location-driven model. Furthermore, we discuss the integration of these three models. Finally, by using these user-profiling models, we propose a new application for geo-social recommendations. In contrast to related studies, which focus on popular and famous points of interests (POIs), our models help discover obscure POIs that are not well known. Experimental evaluations and analysis on a real dataset collected from three cities demonstrate the performance of the proposed user-profiling models. To verify the effect of discovering obscure POIs, the proposed application was implemented to discover and explore obscure POIs in Kyoto, Japan.
Rights: The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-016-4034-6.
The full-text file will be made open to the public on 01 February 2018 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
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。
This is not the published version. Please cite only the published version.
URI: http://hdl.handle.net/2433/219134
DOI(Published Version): 10.1007/s11042-016-4034-6
Appears in Collections:Journal Articles

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