MainTUL

PyTorch implementation for paper: Mutual Distillation Learning Network for Trajectory-User Linking (IJCAI'22)

Dependencies

• 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

Datasets

Raw data：

• Foursquare: http://sites.google.com/site/yangdingqi/home/foursquare-dataset
• Weeplaces: http://www.yongliu.org/datasets.html

Prepocessed data：

• 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).

Usage

• 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

Parameter setting of baselines

• 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)

Citation

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}
}

Acknowledgement