Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation


  • Ke Sun Wuhan University
  • Tieyun Qian Wuhan University
  • Tong Chen The University of Queensland
  • Yile Liang Wuhan University
  • Quoc Viet Hung Nguyen Griffith University
  • Hongzhi Yin The University of Queensland




Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.




How to Cite

Sun, K., Qian, T., Chen, T., Liang, Y., Nguyen, Q. V. H., & Yin, H. (2020). Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 214-221. https://doi.org/10.1609/aaai.v34i01.5353



AAAI Technical Track: AI and the Web