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

Tao Xu, Yutao Ma and Qian Wang
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Tao Xu: Wuhan University, Wuhan, China
Yutao Ma: Wuhan University, Wuhan, China & WISET Automation Co., Ltd., Wuhan, China
Qian Wang: Wuhan University, Wuhan, China

International Journal of Web Services Research (IJWSR), 2018, vol. 15, issue 3, 82-102

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:15:y:2018:i:3:p:82-102

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