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A graph based method for constructing popular routes with check-ins

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Abstract

Location-based social networks allow people to share their experience of trips by check-in or other ways. Check-in records normally contain two aspects of information, one is semantic information (in the form of text) and the other is location information (in the form of coordinate). In this paper, we present a popular route construction method named GRID based on collective knowledge. Firstly, we mine the check-in records which contain the route’s attributes to divide the whole space into regions and find the POIs of each region. Secondly, we use the location trajectory information of check-ins to infer the connection of POIs in the same region and the connection between regions. Finally, according to user-specified query locations a visiting sequence is determined by calculating the probability of each query location. Experimental results on two real datasets show our approach outperforms a state-of-the-art method in terms of effectiveness and efficiency.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (grants No. 61672133, No. 61632007 and No. 61602087), and the Fundamental Research Funds for the Central Universities (grants No. ZYGX2015J058 and No. ZYGX2014Z007).

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Authors and Affiliations

  1. Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Gang Hu, Jie Shao, Zhiyang Ni & Dongxiang Zhang

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  1. Gang Hu
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  2. Jie Shao
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  3. Zhiyang Ni
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  4. Dongxiang Zhang
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Corresponding author

Correspondence to Jie Shao.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Hu, G., Shao, J., Ni, Z. et al. A graph based method for constructing popular routes with check-ins. World Wide Web 21, 1689–1703 (2018). https://doi.org/10.1007/s11280-017-0511-8

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  • DOI: https://doi.org/10.1007/s11280-017-0511-8

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