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Exploiting user behavior learning for personalized trajectory recommendations

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Abstract

With increasing popularity of mobile devices and flourish of social networks, a large number of trajectory data is accumulated. Trajectory data contains a wealth of information, including spatiality, time series, and other external descriptive attributes (i.e., travelling mode, activities, etc.). Trajectory recommendation is especially important to users for finding the routes meeting the user’s travel needs quickly. Most existing trajectory recommendation works return the same route to different users given an origin and a destination. However, the users’ behavior preferences can be learned from users’ historical multi-attributes trajectories. In this paper, we propose two novel personalized trajectory recommendation methods, i.e., user behavior probability learning based on matrix decomposition and user behavior probability learning based on Kernel density estimation. We transform the route recommendation problem to a shortest path problem employing Bayesian probability model. Combining the user input (i.e., an origin and a destination), the trajectory query is performed on a behavior graph based on the learned behavior probability automatically. Finally, a series of experiments on two real datasets validate the effectiveness of our proposed methods.

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Acknowledgements

This work was partially supported by the grant from the Natural Science Foundation of Hebei Province (F2021210005), the Hebei Province Innovation Capability Improvement Plan (21550803D), the Outstanding Youth Foundation of Hebei Education Department (BJ2021085), the Fourth Outstanding Youth Foundation of Shijiazhuang Tiedao University, and Training Project for Improving Students of Scientific and Technological Innovation Ability for College and Middle School (DXS202106), Scientific Research Project from China Railway Corporation (2020F026).

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Authors and Affiliations

  1. School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Xiao Pan, Lei Wu, Fenjie Long & Ang Ma

Authors
  1. Xiao Pan
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  2. Lei Wu
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  3. Fenjie Long
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  4. Ang Ma
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Corresponding author

Correspondence to Lei Wu.

Additional information

Xiao Pan is an associate professor in Shijiazhuang Tiedao University. China. She received her PhD degree from Renmin University of China, China. Her research interests include data management, mobilecomputing and privacy-aware computing. She is a member of the Database Society of the China Computer Federation (CCF DBS).

Lei Wu is a lecturer at Shijiazhuang Tiedao University, and is a PhD candidate at Yanshan University. China. His research interests include spatial-temporal data management and mining.

Fenjie Long is a professor in Shijiazhuang Tiedao University. China. He received his PhD degree from Peking University, China. He served as a visiting scholar in The City University of New York, USA in 2012. His research interest is the urbanization and urban problems.

Ang Ma received the Master degree and BS degree from Shijiazhuang Tiedao University, China in 2019 and 2016, respectively. Her research interests include data mining and mobile computing.

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Pan, X., Wu, L., Long, F. et al. Exploiting user behavior learning for personalized trajectory recommendations. Front. Comput. Sci. 16, 163610 (2022). https://doi.org/10.1007/s11704-020-0243-2

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