Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

  • Pengpeng Zhao Soochow University
  • Haifeng Zhu Soochow University
  • Yanchi Liu Rutgers University
  • Jiajie Xu Soochow University
  • Zhixu Li Soochow University
  • Fuzhen Zhuang Chinese Academy of Sciences
  • Victor S. Sheng University of Central Arkansas
  • Xiaofang Zhou University of Queensland

Abstract

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning