Any question, please mail to xc_chen@ruc.edu.cn
To run the Tax-rank model, you need to run the run_tax-rank.py.(with default setting)
python run_tax-rank.py
Result should be:
GINI:0.968 ACC:6.266
If you want to change the experimental setting, you can choose to determine the parameters as followed:
| parameter | type | range | detail |
|---|---|---|---|
| U | int | [1 ,+inf) | The size of users of the dataset you want to select |
| I | int | [1 ,+inf) | The size of items of the dataset you want to select |
| mode | str | {'ad', 'rec'} | 'ad' if you want to run Tax-rank on the advertising dataset; 'rec' if you want to run Tax-rank on the recommendation dataset |
| topk | int | [1,U] | the size of the recommendation list |
| t | float | [0,+inf) | the taxation rate of Tax-rank to trade-off between accuracy and fairness |
| k | float | (0, +inf) | the hyperparameter to determine the size of eta |
| lbd | float | (0,+inf) | the coefficient in OT projection to determine the smoothness and the convexity of the distribution OT solution |
Here we give a example of run the model using self-determined parameter setting and a small set of dataset:
python run_tax-rank.py --U=503 --I=314 --mode=rec --topk=10 --t=1 --k=0.1 --lbd=1 For entire dataset, please download from urls from paper.
##For citation, please cite the following bib
@inproceedings{Xu-TaxRank-SIGIR24,
author = {Xu, Chen and Ye, Xiaopeng and Wang, Wenjie and Pang, Liang and Xu, Jun and Ji-rong Wen},
title = {A Taxation Perspective for Fair Re-ranking},
year = {2024},
isbn = {979-8-4007-0431-4/24/07},
publisher = {Association for Computing Machinery},
address = {Washington, DC, USA},
doi = {10.1145/3543507.3583296},
booktitle = {Proceedings of the 47th
International ACM SIGIR Conference on Research and Development in
Information Retrieval},
series = {SIGIR '24}
}