yule-BUAA / CTTSP

codes of Continuous-Time User Preference Modelling for Temporal Sets Prediction

Continuous-Time User Preference Modelling for Temporal Sets Prediction

The description of "Continuous-Time User Preference Modelling for Temporal Sets Prediction" is available here.

Original data:

The original data could be downloaded from here. You can download the data and then put the data files in the ./original_data folder.

To run the code:

1. run ./preprocess_data/preprocess_data_{dataset_name}.py to preprocess the original data, where dataset_name can be JingDong, DC, TaFeng and TaoBao. We also provide the preprocessed datasets at here, which should be put in the ./dataset folder.

2. run ./train/train_CTTSP.py to train the model on different datasets using the configuration in ./utils/config.json.

3. run ./evaluate/evaluate_CTTSP.py to evaluate the model. Please make sure the config in evaluate_CTTSP.py keeps identical to that in the model training process.

Environments:

• PyTorch 1.8.1
• tqdm
• numpy
• pandas

Hyperparameter settings:

Hyperparameters can be found in ./utils/config.json file, and you can adjust them when training the model on different datasets.

Hyperparameters	JingDong	DC	TaFeng	TaoBao
learning rate	0.001	0.001	0.001	0.001
dropout rate	0.2	0.2	0.15	0.05
embedding dimension	64	64	64	32
user perspective importance	0.9	0.5	0.05	0.9
continuous-time probability importance	0.9	0.0	0.7	0.7

Citation

Please consider citing our paper when using this project.

@article{yu2022continuous,
  title={Continuous-Time User Preference Modelling for Temporal Sets Prediction},
  author={Yu, Le and Liu, Zihang and Sun, Leilei and Du, Bowen and Liu, Chuanren and Lv, Weifeng},
  journal={arXiv preprint arXiv:2204.05490},
  year={2022}
}