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Modeling User Mobility via User Psychological and Geographical Behaviors Towards Point of-Interest Recommendation

  • Yan Chen
  • Xin Li
  • Lin Li
  • Guiquan LiuEmail author
  • Guangdong Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)

Abstract

The pervasive employments of Location-based Social Network call for precise and personalized Point-of-Interest (POI) recommendation to predict which places the users prefer. Modeling user mobility, as an important component of understanding user preference, plays an essential role in POI recommendation. However, existing methods mainly model user mobility through analyzing the check-in data and formulating a distribution without considering why a user checks in at a specific place from psychological perspective. In this paper, we propose a POI recommendation algorithm modeling user mobility by considering check-in data and geographical information. Specifically, with check-in data, we propose a novel probabilistic latent factor model to formulate user psychological behavior from the perspective of utility theory, which could help reveal the inner information underlying the comparative choice behaviors of users. Geographical behavior of all the historical check-ins captured by a power law distribution is then combined with probabilistic latent factor model to form the POI recommendation algorithm. Extensive evaluation experiments conducted on two real-world datasets confirm the superiority of our approach over state-of-the-art methods.

Keywords

Location-based social network Point-of-Interest recommendation User psychological behavior Geographical behavior User mobility 

Notes

Acknowledgments

This research was partially supported by grants from the Science and Technology Program for Public Wellbeing of China (Grant No. 2013GS340302), National Natural Science Fund Project of China (Grant No. 61232018 and 61325010), National Social and Science Fund project of China (Grant No. 15BGL048), National 863 Plan Project of China (Grant No. 2015AA015403) and Hubei Province Support project of China (Grant No. 2015BAA072).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yan Chen
    • 1
  • Xin Li
    • 2
  • Lin Li
    • 3
  • Guiquan Liu
    • 1
    Email author
  • Guangdong Xu
    • 4
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.iFlyTek ResearchHefeiChina
  3. 3.Wuhan University of TechnologyWuhanChina
  4. 4.University of Technology SydneySydneyAustralia

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