The description of "Predicting Temporal Sets with Simplified Fully Connected Networks" at AAAI 2023 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
could be JingDong, DC, TaoBao and TMS. We also provide the preprocessed datasets at here, which should be put in the./dataset
folder. -
run
./train/train_SFCNTSP.py
to train the model and get the results on different datasets according to the configuration in./utils/config.json
.
Hyperparameters can be found in ./utils/config.json
file, and you can adjust them when training the model on different datasets.
Hyperparameters | JingDong | DC | TaoBao | TMS |
---|---|---|---|---|
learning rate | 0.001 | 0.001 | 0.001 | 0.001 |
dropout rate | 0.2 | 0.1 | 0.05 | 0.1 |
embedding channels | 64 | 64 | 32 | 64 |
alpha | 1.0 | 1.0 | 1.0 | 1.0 |
beta | 0.1 | 0.1 | 0.1 | 0.1 |
Please consider citing our paper when using the codes or datasets.
@inproceedings{yu2023predicting,
title={Predicting Temporal Sets with Simplified Fully Connected Networks},
author={Yu, Le and Liu, Zihang and Zhu, Tongyu and Sun, Leilei and Du, Bowen and Lv, Weifeng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={4},
pages={4835--4844},
year={2023}
}