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A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

  • Hengpeng Xu
  • Peizhi Wu
  • Jinmao WeiEmail author
  • Zhenglu YangEmail author
  • Jun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)

Abstract

Predicting next point of interest (POI) of users in location-based social networks has become an increasingly significant requirement, because of its potential benefits for individuals and businesses. Recently, various recurrent neural network architectures have incorporated contextual information associated with users’ sequence of check-ins to capture their dynamic preferences. However, these architectures are limited because they only take the sequential order of check-ins into account and face difficulties in remembering long-range dependencies. In this work, we resort to the heterogeneous of information network (HIN) to address these issues. Specifically, a novel attentional meta-path-based recurrent neural network is proposed, dubbed ST-HIN. ST-HIN predicts the next POI of users from their spatial–temporal incomplete historical check-in sequences, and uses the multi-modal recurrent neural network to capture the complex transition relationship. Furthermore, a meta-path attention embedding module is devised to capture the mutual influence between the users’ meta-path-based global information in HIN and the dynamic status of their current mobility. The results of extensive experiments performed on real-world datasets demonstrate the effectiveness of our proposed model.

Keywords

POI prediction Meta-path Recurrent model Spatial context 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceNankai UniversityTianjinChina
  2. 2.College of Mathematics and Statistics ScienceLudong UniversityShandongChina

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