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Survey on user location prediction based on geo-social networking data

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Abstract

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.

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Notes

  1. In this article, the three terms ‘location’, ‘POI’ and ‘venue’ can be used interchangeably unless otherwise stated.

  2. https://foursquare.com/

  3. https://www.yelp.com/

  4. https://www.cnbc.com/2017/08/30/foursquare-pioneered-the-trend-of-checking-in-to-a-place--now-it-sells-your-data-to-companies.html

  5. https://www.yelp.com/about

  6. https://api.foursquare.com/v1/categories

  7. https://www.yelp.com/dataset/challenge

  8. Tabelog is a restaurant information website for those who want to choose the right restaurant for their needs.

  9. A famous travelogue website offering rich descriptions about landmarks and traveling experience written by users.

  10. Code of SAE-NAD is available at https://github.com/allenjack/SAE-NAD; Code of CARA is available at https://github.com/feay1234/CARA; Code of LBSN2Vec is available at https://github.com/eXascaleInfolab/LBSN2Vec

  11. Yelp dataset challenge round 12, https://www.yelp.com/dataset/challenge, access date: January 2019.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grants No. 61772133, No.61472081, No. 61402104. Jiangsu Provincial Key Project BE2018706. Jiangsu Provincial Key Laboratory of Computer Networking Technology, Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No. BM2003201, and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No. 93K-9.

Besides, the financial support provided by China Scholarship Council (CSC) during a visit of Shuai Xu to University of Goettingen (Germany) is acknowledged. Zhixiao Wang is supported in part by the grants: Youth Fund of the Ministry of Education of China (No.16YJCZH109).

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  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Shuai Xu & Bo Liu

  2. Institute of Computer Science, University of Goettingen, Goettingen, Germany

    Xiaoming Fu

  3. Jiangsu Provincial Key Laboratory of Computer Networking Technology, School of Cyber Science and Engineering, Southeast University, Nanjing, China

    Jiuxin Cao

  4. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Zhixiao Wang

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  1. Shuai Xu
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Correspondence to Jiuxin Cao.

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This work was finished when Shuai Xu visited University of Goettingen, Germany.

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Xu, S., Fu, X., Cao, J. et al. Survey on user location prediction based on geo-social networking data. World Wide Web 23, 1621–1664 (2020). https://doi.org/10.1007/s11280-019-00777-8

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