PyTorch implementation for paper: Mutual Distillation Learning Network for Trajectory-User Linking (IJCAI'22)
- Python 3.9
- torch==1.10.1 (cuda10.2)
- scikit-learn==1.0.1
- tqdm==4.62.3
- pandas==1.3.4
- matplotlib==3.5.0
- Foursquare: http://sites.google.com/site/yangdingqi/home/foursquare-dataset
- Weeplaces: http://www.yongliu.org/datasets.html
- The prepocessed data is uploaded in the folder data.
- We provide two forms for each dataset, one is full data and the other is sampled small data (for quick testing).
- Preprocess original data. (If you want to use other dataset, please preprocess the dataset into the data format under the folder data.)
- Run main.py
- Adjust the hyperparameters and strategies according to the needs
- e.g.
python main.py --temperature 0.1/2/5/10/15 --lambda_parm 1/5/10/15/20
- e.g.
-
Tradition Methods:
- DT: (Entropy)
- LDA: (LDA Matrix solver: SVD)
- LCSS
- SR: (spatial signature / Reduced (m = 10))
-
Deep Learning Methods:
- TULER and its variants: (LR: 0.00095 / Dimension: 250 / Hidden size: 300 / Dropout rate: 0.5 / Layers: 2)
- TULVAE: (LR: 0.001 / decays: 0.9 /
$\beta$ : 0.5-1 / POI Dimension: 250 / Hidden size: 300 / Latent Variable Dimension: 100) - DeepTUL: (
$D_p$ : 64 /$D_t$ : 32 /$D_u$ : 32 / LR: 0.005/ decays: 0.5 / time interval: 120)
If you want to use our codes in your research, please cite:
@inproceedings{chen2022MainTUL,
title={Mutual Distillation Learning Network for Trajectory-User Linking},
author={Chen, Wei and Li, Shuzhe and Huang, Chao and Yu, Yanwei and Jiang, Yongguo and Dong, Junyu},
booktitle={IJCAI},
year={2022}
}