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

By K. Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin. AAAI Conference on Artificial Intelligence, 2020

428 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b005c990ece2c9c287772937c7badd2639d30029

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In Computer Vision and Pattern Recognition

  1. Yingtao Luo
  2. Zhaocheng Liu
  3. Qiang Liu
  4. Michalis K. Titsias
  5. Jun Zhu
  6. Jiaxin Shi
  7. Nicholas Zabaras
  8. Dilin Wang

In Information Systems

  1. Yingtao Luo
  2. Zhaocheng Liu
  3. Qiang Liu

In Signal Processing

  1. Qiang Liu

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