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Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

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Abstract

In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information (such as time, current location, and friends’ preferences). We develop a spatial-temporal topic model to describe users’ location interest, based on which we form comprehensive feature representations of user interests and contextual information. We propose a supervised RNN learning prediction model for next POI recommendation. Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach, and achieve best F1-score of 0.196 754 on the Gowalla dataset and 0.354 592 on the Brightkite dataset.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Ming Chen, Wen-Zhong Li, Lin Qian, Sang-Lu Lu & Dao-Xu Chen

  2. Sino-German Institutes of Social Computing, Nanjing University, Nanjing, 210023, China

    Wen-Zhong Li & Sang-Lu Lu

  3. State Grid Electric Power Research Institute, NARI Group Corporation, Nanjing, 211000, China

    Lin Qian

Authors
  1. Ming Chen
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  2. Wen-Zhong Li
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  3. Lin Qian
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  4. Sang-Lu Lu
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  5. Dao-Xu Chen
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Corresponding authors

Correspondence to Wen-Zhong Li or Sang-Lu Lu.

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Chen, M., Li, WZ., Qian, L. et al. Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks. J. Comput. Sci. Technol. 35, 603–616 (2020). https://doi.org/10.1007/s11390-020-9107-3

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  • DOI: https://doi.org/10.1007/s11390-020-9107-3

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