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Research on User Behavior Prediction and Profiling Method Based on Trajectory Information

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Abstract—

Aiming at the need to discover user behavior characteristics and knowledge from moving trajectory data, a user behavior profiling method based on moving trajectory information was proposed. Firstly, the trajectory coordinates were preprocessed to clean out good data. Secondly, the travel rules and the points of interest of the user were found by means of stay points detection, staying points’ semantics and frequent pattern mining. In the aspect of predicting user trajectory information, Key Points Long Short-Term Memory Networks (KP-LSTM) was proposed to predict the user’s future travel location; then the user’s important attribute characteristics were taken through the user profiling, intuitively depicting the characteristics and patterns of users’ lives. Finally, the availability of the method was proved by experiments, and the prediction accuracy was better than the traditional Linear regression and LSTM neural network.

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Funding

This work is partially supported by Natural Science Foundation of China no. 61370139, fund of Bistu improving the scientific research level no. 5 211 910 933, and “Information +” special fund.

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Authors and Affiliations

  1. Computer School, Beijing Information Science and Technology University, 100192, Beijing, China

    Hao Li

  2. School of Information Management, Beijing Information Science and Technology University, 100192, Beijing, China

    Haiyan Kang

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  1. Hao Li
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  2. Haiyan Kang
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Correspondence to Haiyan Kang.

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Hao Li, Haiyan Kang Research on User Behavior Prediction and Profiling Method Based on Trajectory Information. Aut. Control Comp. Sci. 54, 456–465 (2020). https://doi.org/10.3103/S0146411620050065

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