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Learning evolving user’s behaviors on location-based social networks

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Abstract

With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper, we propose a new dynamic model that considers both geo-aware user preferences and the social interaction excitation arising from social connections to learn the dynamic mobility mechanism of user’s behaviors on LBSNs. Geo-aware location features, such as semantic features, latent features and dynamic features, are utilized to characterize the location information and reveal the evolution of the geographical impact of location. These geo-aware location features enable us to exploit user’s personal preferences. Meanwhile, we integrate a user’s social connections and friends’ preferences for modeling social interaction excitations. Finally, we jointly incorporate geo-aware user preference learning and social interaction excitation modeling to create a conditional intensity function for temporal point processes with which to explore the dynamic mobility mechanism of evolving user’s check-in behaviors on LBSNs. Extensive experiments on several real-world check-in datasets confirm that our proposed algorithm performs better than existing state-of-the-art methods.

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Acknowledgements

This work was supported in part China Scholarship Council (201806070074), in part by the Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (161062), in part by National Key Research and Development Program under Grant (2016YFB0502300), in part by Key Research Plan for State Comission of Science Technology of China (2018YFC0807501), in part by Sichuan Science and Technology Program (No.2017JZ0031,2018JY0578,2018JY0067), and Chengdu Science and Technology Bureau Program (2018-YF09-00051-SN).

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Ruizhi Wu, Qi Jin & Junming Shao

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Guangchun Luo

  3. Chengdu Municipal Public Security Bureau, Chengdu, China

    Qi Jin

  4. Department of Computer Science, Virginia Tech, Falls Church, VA, USA

    Chang-Tien Lu

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  1. Ruizhi Wu
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  2. Guangchun Luo
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Correspondence to Guangchun Luo.

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Wu, R., Luo, G., Jin, Q. et al. Learning evolving user’s behaviors on location-based social networks. Geoinformatica 24, 713–743 (2020). https://doi.org/10.1007/s10707-020-00400-3

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