Understanding Human Mobility from Geographical Perspective

  • Shenglin ZhaoEmail author
  • Michael R. Lyu
  • Irwin King
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


POI recommendation is a significant service for LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users’ interests depends on three factors: user preference, social influence, and geographical influence. Especially, capturing the geographical influence plays the most important role for POI recommendations. Previous studies observe that checked-in locations disperse around several centers and employ Gaussian distribution based models to approximate users’ check-in behaviors. Yet centers discovering methods are not satisfactory in prior work. This chapter shows how to exploit Gaussian mixture model (GMM) and genetic algorithm based Gaussian mixture model (GA-GMM) to capture geographical influence. Experimental results on a real-world LBSN dataset show that GMM beats several popular geographical capturing models in terms of POI recommendation, while GA-GMM excludes the effect of outliers and enhances GMM.


POI Recommendation Geographical influence Gaussian mixture model 


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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Youtu LabTencentShenzhenChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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