Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction

  • Qiang Cui
  • Yuyuan Tang
  • Shu WuEmail author
  • Liang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)


Point-of-interest (POI) prediction is a key task in location-based social networks. It captures the user preference to predict POIs. Recent studies demonstrate that spatial influence is significant for prediction. The distance can be converted to a weight reflecting the relevance of two POIs or can be utilized to find nearby locations. However, previous studies almost ignore the correlation between user and distance. When people choose the next POI, they will consider the distance at the same time. Besides, spatial influence varies greatly for different users. In this work, we propose a Distance-to-Preference (Distance2Pre) network for the next POI prediction. We first acquire the user’s sequential preference by modeling check-in sequences. Then, we propose to acquire the spatial preference by modeling distances between successive POIs. This is a personalized process and can capture the relationship in user-distance interactions. Moreover, we propose two preference encoders which are a linear fusion and a non-linear fusion. Such encoders explore different ways to fuse the above two preferences. Experiments on two real-world datasets show the superiority of our proposed network.


POI Sequential preference Spatial preference Non-linear 



This work is jointly supported by National Natural Science Foundation of China (61772528), National Key Research and Development Program (2016YFB1001000), National Natural Science Foundation of China (U1435221).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Science and Technology BeijingBeijingChina

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