Location Recommendation Based on Mobility Graph With Individual and Group Influences

Xuan Pan, Xiangrui Cai, Kehui Song, Thar Baker, Thippa Reddy Gadekallu, Xiaojie Yuan

Research output: Contribution to journalArticlepeer-review


With the rapid development of mobile technology, it is very convenient to share people's current locations by checking-in on Location-Based Social Networks (LBSNs). Using users' check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users' mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusPublished - 16 Feb 2022


  • Analytical models
  • Context modeling
  • Data models
  • Deep learning
  • Encoding
  • History
  • Location recommendation
  • Predictive models
  • graph neural networks
  • location based social networks
  • mobility behaviors.


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