Geographical Constraint and Temporal Similarity Modeling for Point-of-Interest Recommendation

  • Huimin Wu
  • Jie ShaoEmail author
  • Hongzhi Yin
  • Heng Tao Shen
  • Xiaofang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9419)


People often share their visited Points-of-Interest (PoIs) by “check-ins”. On the one hand, human mobility varies with each individual but still implies regularity. Check-ins of an individual tend to localize in a specific geographical range. We propose a novel model to capture personalized geographical constraint of each individual. On the other hand, PoIs reflect requirements of people from different aspects. Usually, places of different functions show different temporal visiting distributions and places of similar function share similar visiting pattern in temporal aspect. Temporal distribution similarity can be used to characterize functional similarity. Based on the findings above, this paper introduces improved collaborative filtering models by jointly taking advantages of geographical constraint and temporal similarity. Experimental results on real data collected from Gowalla and JiePang demonstrate the effectiveness of our models.


Recommendation system Collaborative filtering Geographical constraint Temporal similarity 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Huimin Wu
    • 1
  • Jie Shao
    • 1
    Email author
  • Hongzhi Yin
    • 2
  • Heng Tao Shen
    • 2
  • Xiaofang Zhou
    • 2
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.The University of QueenslandBrisbaneAustralia

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