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Memory Augmented Hierarchical AttentionNetwork for Next POI Recommendation

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# Memory Augmented Hierarchical AttentionNetwork for Next Point-of-Interest Recommendation

This is our Pytorch implementation for the paper:

Chenwang Zheng, Dan Tao*, Jiangtao Wang, Lei Cui, Wenjie Ruan, and Shui Yu. Memory Augmented Hierarchical AttentionNetwork for Next Point-of-Interest Recommendation. IEEE Transactions on Computational Social System. 2020

Please cite our paper if you use the code or datasets.

# Requirement
- python==3.6    
- pytorch-gpu==1.3.0

# Dataset
We use the real-world LBSN datasets from Foursquare and Gowalla:

- Foursquare: [http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/](http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/)
- Gowalla: [http://www.yongliu.org/datasets/index.html](http://www.yongliu.org/datasets/index.html)

# Codes of Compared Methods

- FPMC-LR, PRME-G and POI2Vec: [https://github.com/cuiqiang1990/Distance2Pre](https://github.com/cuiqiang1990/Distance2Pre)
- ST-RNN: [https://github.com/yongqyu/STRNN](https://github.com/yongqyu/STRNN)
- SASRec: [https://github.com/kang205/SASRec](https://github.com/kang205/SASRec)
- AttRec: [https://github.com/cheungdaven/DeepRec](https://github.com/cheungdaven/DeepRec)

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