This repository contains the implementation for paper: Joint Multisided Exposure Fairness for Recommendation (SIGIR 2022).
The data contains the datasets we used from here.
The saved_model contains the pre-trained model from here.
The read_data.py contains the data reading and preprocessing.
The Disparity_Metrics.py contains the implementation of our proposed JME-Fairness metrics.
The run_metric.py outputs the output values for different JME-Fairness metrics.
python run_metric.py
If you find this code or idea useful, please cite our work:
@inproceedings{wu2022joint,
title={Joint Multisided Exposure Fairness for Recommendation},
author={Wu, Haolun and Mitra, Bhaskar and Ma, Chen and Diaz, Fernando and Liu, Xue},
booktitle={SIGIR},
publisher = {{ACM}},
year={2022}
}
If you have any questions, feel free to contact us through email (haolun.wu@mail.mcgill.ca) or Github issues. Enjoy!