The official PyTorch implementation for Multi-View Spatial-Temporal Enhanced Hypergraph Network for Next POI Recommendation, which is accepted to DASFAA2023.
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{lai2023multi,
title={Multi-view Spatial-Temporal Enhanced Hypergraph Network for Next POI Recommendation},
author={Lai, Yantong and Su, Yijun and Wei, Lingwei and Chen, Gaode and Wang, Tianci and Zha, Daren},
booktitle={Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17--20, 2023, Proceedings, Part II},
pages={237--252},
year={2023},
organization={Springer}
}
Multi-View Spatial-Temporal Enhanced Hypergraph Network (MSTHN) designs a local spatial-temporal enhanced graph neural network,
to capture important spatial-temporal correlations during aggregation and propagation, and a global interactive hypergraph neural network
to uncover complex high-order collaborative signals and alleviate check-in data sparsity issue for next POI recommendation.
python==3.7
torch==1.9.1
- Foursquare dataset is collected by Yang et al. 2015, and could be downloaded from link
Dingqi Yang, Daqing Zhang, Vincent W. Zheng, Zhiyong Yu. Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs. IEEE Trans. on Systems, Man, and Cybernetics: Systems, (TSMC), 45(1), 129-142, 2015
- Gowalla dataset is collected by Yin et al. 2015, and could be downloaded from link
Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. ACM Transactions on Knowledge Discovery from Data (TKDD) 9(3), 1–41 (2015)
python run.py --dataset NYC
python run.py --dataest TKY
python run.py --dataset Gowalla
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