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Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method

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Abstract

As the cornerstone of location-based services, location prediction aims to predict user’s next location through modeling user’s personal preference or travel sequential pattern. However, most existing methods only consider one of them and extremely sparse data makes it difficult to dynamically and comprehensively characterize user preference. In this paper, we propose a novel Dynamic Spatiotemporal User Preference (DSUP) model to characterize dynamic spatiotemporal user preference and integrate it with user’s travel sequential pattern for location prediction. Specifically, we design an interaction-aware graph attention network to learn the embeddings of locations and timeslots, and infer dynamic spatiotemporal user preference from the history travel locations and timeslots. Then, we combine user’s current travel preference with the impact of history travel sequential pattern to predict user’s next location. In addition, we predict user’s next travel timeslot and combine it with the temporal pattern of locations to enhance the location and timeslot prediction results mutually. We conduct extensive experiments on two public datasets Gowalla, Foursquare and our own Private Car dataset. The results on three datasets show that our method improves the accuracy and mean reciprocal rank of location prediction by 3%-11% and 7%-10% respectively.

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Availability of data and materials

All the datasets adopted in the research are available from corresponding authors upon request.

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Funding

This work was partly supported by NSFC under Grant 62272152, the National Key Research and Development Program of China under Grant 2023YFC3321601, the Humanities and Social Sciences Foundation of Ministry of Education under Grant 21YJCZH183, the Key R &D Program of Hunan Province under Grants 2021WK2001 and 2022GK2020, the Hunan Natural Science Foundation of China under Grant 2022JJ30171, the Shenzhen Science and Technology Program under Grant JCYJ20220530160408019, the CAAI-Huawei MindSpore Open Fund, and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011915.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Jiawei Cai, Dong Wang, Chenxi Liu & Zhu Xiao

  2. Research Center for Graph Computing, Zhejiang Lab, Hangzhou, China

    Hongyang Chen

  3. Shenzhen Research Institute of Hunan University, Shenzhen, China

    Zhu Xiao

Authors
  1. Jiawei Cai
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  2. Dong Wang
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  3. Hongyang Chen
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  4. Chenxi Liu
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  5. Zhu Xiao
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Contributions

All authors contributed to technical discussion, Jiawei Cai wrote the main manuscript text, Jiawei Cai and Dong Wang performed the algorithm design, Jiawei Cai, Hongyang Chen and Chenxi Liu performed the data processing, Zhu Xiao helped revised the manuscript, all author reviewed the manuscript and give constructive suggestion.

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Correspondence to Dong Wang or Zhu Xiao.

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Cai, J., Wang, D., Chen, H. et al. Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method. World Wide Web 27, 14 (2024). https://doi.org/10.1007/s11280-024-01245-8

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