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The official PyTorch implementation for 2024-ICASSP-Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI Recommendation

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ICASSP2024_ASTHL

The official PyTorch implementation for Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI Recommendation, which is accepted to ICASSP'2024 as full paper for oral presentation.

If you have any questions, please feel free to issue or contact me by email. If you use our codes and datasets in your research, please cite:

@inproceedings{lai2024adaptive,
  title={Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI Recommendation},
  author={Lai, Yantong and Su, Yijun and Wei, Lingwei and Wang, Tianci and Zha, Daren and Wang, Xin},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7320--7324},
  year={2024},
  organization={IEEE}
}

Overview

Next point-of-interest (POI) recommendation has been a trending task to provide next POI suggestions. Most existing sequential-based and graph-based methods have endeavored to model user visiting behaviors and achieved considerable performances. However, they have either modeled user interests at a coarse-grained interaction level or ignored complex high-order feature interactions through general heuristic message passing scheme, making it challenging to capture complementary effects. To tackle these challenges, we propose a novel framework Adaptive Spatial-Temporal Hypergraph Fusion Learning (ASTHL) for next POI recommendation. Specifically, we design disentangled POI-centric learning to decouple spatial-temporal factors and utilize cross-view contrastive learning to enhance the quality of POI representations. Furthermore, we propose multi-semantic enhanced hypergraph learning to adaptively fuse spatial-temporal factors through well-designed aggregation and propagation scheme. Extensive experiments on three real-world datasets validate the superiority of our proposal over various state-of-the-arts.

ASTHL Model

Requirements

torch==1.12.0

Running

nohup python3 -u run.py --dataset NYC >> NYC.out
nohup python3 -u run.py --dataset TKY >> TKY.out
nohup python3 -u run.py --dataset Gowalla >> Gowalla.out

License

MIT License

Copyright (c) 2024 Yantong Lai

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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