English  |  正體中文  |  简体中文  |  Items with full text/Total items : 88266/117736 (75%)
Visitors : 23393235      Online Users : 142
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/111610
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111610

    Title: Exploring check-in data to infer social ties in location based social networks
    Authors: Njoo, Gunarto Sindoro;Kao, Min-Chia;Hsu, Kuo-Wei;Peng, Wen-Chih
    Contributors: 資訊科學系
    Keywords: Location;Network function virtualization;Social networking (online);Derived features;Location data;Location-based social networks;Mobility pattern;Social connection;Social networking services;Spatial-temporal features;State-of-the-art methods;Data mining
    Date: 2017
    Issue Date: 2017-08-02 16:07:28 (UTC+8)
    Abstract: Social Networking Services (SNS), such as Facebook, Twitter, and Foursquare, allow users to perform check-in and share their location data. Given the check-in data records, we can extract the features (e.g., the spatial-temporal features) to infer the social ties. The challenge of this inference task is to differentiate between real friends and strangers by solely observing their mobility patterns. In this paper, we explore the meeting events or co-occurrences from users’ check-in data. We derive three key features from users’ meeting events and propose a framework called SCI framework (Social Connection Inference framework) which integrates all derived features to differentiate coincidences from real friends’ meetings. Extensive experiments on two location-based social network datasets show that the proposed SCI framework can outperform the state-of-the-art method. © 2017, Springer International Publishing AG.
    Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10234 LNAI, 460-471
    Data Type: book/chapter
    DOI 連結: http://dx.doi.org/10.1007/978-3-319-57454-7_36
    DOI: 10.1007/978-3-319-57454-7_36
    Appears in Collections:[資訊科學系] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback