The description of "Continuous-Time User Preference Modelling for Temporal Sets Prediction" is available here.
The original data could be downloaded from here.
You can download the data and then put the data files in the ./original_data
folder.
-
run
./preprocess_data/preprocess_data_{dataset_name}.py
to preprocess the original data, wheredataset_name
can be JingDong, DC, TaFeng and TaoBao. We also provide the preprocessed datasets at here, which should be put in the./dataset
folder. -
run
./train/train_CTTSP.py
to train the model on different datasets using the configuration in./utils/config.json
. -
run
./evaluate/evaluate_CTTSP.py
to evaluate the model. Please make sure theconfig
inevaluate_CTTSP.py
keeps identical to that in the model training process.
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 |
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}
}