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Cross-Urban Point-of-Interest Recommendation for Non-Natives

Author

Listed:
  • Tao Xu

    (Wuhan University, Wuhan, China)

  • Yutao Ma

    (Wuhan University, Wuhan, China & WISET Automation Co., Ltd., Wuhan, China)

  • Qian Wang

    (Wuhan University, Wuhan, China)

Abstract

This article describes how understanding human mobility behavior is of great significance for predicting a broad range of socioeconomic phenomena in contemporary society. Although many studies have been conducted to uncover behavioral patterns of intra-urban and inter-urban human mobility, a fundamental question remains unanswered: To what degree is human mobility behavior predictable in new cities—a person has never visited before? Location-based social networks with a large volume of check-in records provide an unprecedented opportunity to investigate cross-urban human mobility. The authors' empirical study on millions of records from Foursquare reveals the motives and behavioral patterns of non-natives in 59 cities across the United States. Inspired by the ideology of transfer learning, the authors also propose a machine learning model, which is designed based on the regularities that they found in this study, to predict cross-urban human whereabouts after non-natives move to new cities. The experimental results validate the effectiveness and efficiency of the proposed model, thus allowing us to predict and control activities driven by cross-urban human mobility, such as mobile recommendation, visual (personal) assistant, and epidemic prevention.

Suggested Citation

  • Tao Xu & Yutao Ma & Qian Wang, 2018. "Cross-Urban Point-of-Interest Recommendation for Non-Natives," International Journal of Web Services Research (IJWSR), IGI Global, vol. 15(3), pages 82-102, July.
  • Handle: RePEc:igg:jwsr00:v:15:y:2018:i:3:p:82-102
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    References listed on IDEAS

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    2. Riccardo Gallotti & Armando Bazzani & Sandro Rambaldi & Marc Barthelemy, 2016. "A stochastic model of randomly accelerated walkers for human mobility," Nature Communications, Nature, vol. 7(1), pages 1-7, November.
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