Source code for our paper: Intersectional Two-sided Fairness in Recommendation, accepted by WWW 2024.
First install the environment dependencies using the following command.
conda install --file requirements.txt
C++ code is used to output accuracy-based metrics during training, as used in LightGCN. Thanks for their code! It needs to be compiled first using the following command:
python setup.py build_ext --inplace
We provide the processed data in the data directory, and you can also choose to re-download the three datasets: Tenrec, Movielens, LastFM, and run the following command to regenerate the data (note that the raw data path in the preprocessing code need to be modified):
cd preprocess
python tenrec_qba.py
python ml1m.py
python lfm2b.py
Run the shell run.sh to reproduce the results of ITFR on the three datasets.
If you find this work is helpful to your research, please consider citing our paper:
@inproceedings{10.1145/3589334.3645518,
author = {Wang, Yifan and Sun, Peijie and Ma, Weizhi and Zhang, Min and Zhang, Yuan and Jiang, Peng and Ma, Shaoping},
title = {Intersectional Two-sided Fairness in Recommendation},
year = {2024},
isbn = {9798400701719},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645518},
doi = {10.1145/3589334.3645518},
numpages = {12},
series = {WWW '24}
}
Please feel free to contact the author at [email protected] for any help.