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HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 14118)


Accurate human mobility prediction is an essential but critical task in location-based services. Although existing deep learning solutions such as deep recurrent neural networks have remarkable achievements for this task, The diversity of check-in preferences and the sparsity of trajectory representations still prevent us from effectively capturing the richness of human mobility intentions and patterns. To this end, this study introduces a novel Hyperspherical Bayesian learning approach for mobility prediction problem, i.e., HBay. As a generative model, HBay considers multiple contextual semantics underlying check-ins to maximize human diverse preferences and encodes human trajectories in a latent space to mimic complex mobility patterns. In contrast to traditional generative models, HBay operates the latent variables derived from human trajectories in the hyperspherical space to avoid the concern of posterior collapse. In addition, HBay couples with an attentive layer to capture human long-term check-in preferences. The experimental results conducted on four real-world datasets demonstrate our HBay significantly outperforms the state-of-the-art baselines.


  • human mobility
  • representation learning
  • varitional inference
  • hyperspherical space
  • attention mechanism

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This work was supported by the National Natural Science Foundation of China (Grant No. 62102326), the Natural Science Foundation of Sichuan Province (Grant No. 2023NSFSC1411), the Key Research and Development Project of Sichuan Province (Grant No. 2022YFG0314), and Guanghua Talent Project.

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Correspondence to Qiang Gao .

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Huang, L., Liu, K., Liu, C., Gao, Q., Zhou, X., Liu, G. (2023). HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham.

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  • Print ISBN: 978-3-031-40285-2

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