FairWork: A Generic Framework For Evaluating Fairness In LLM-Based Job Recommender System.
Teng Guo|Yuhan Hu|Mi Tian|Ziyu Lyu|Lu Bai|Teng Guo|Yuhan Hu|Lixin Cui|Zitao Liu|Mi Tian|Ziyu Lyu|Lixin Cui|Zitao Liu
| Anthology ID: | DBLP:conf/sigir/HuL0C25 |
|---|---|
| Volume: | Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025 |
| Year: | 2025 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 3964-3968 |
| URL: | https://doi.org/10.1145/3726302.3730145 |
| DOI: | https://doi.org/10.1145/3726302.3730145 |
| DBLP: | conf/sigir/HuL0C25 |
| BibTeX: |
@inproceedings{guo-2025-fairwork,
author = {Teng Guo and
Yuhan Hu and
Mi Tian and
Ziyu Lyu and
Lu Bai and
Teng Guo and
Yuhan Hu and
Lixin Cui and
Zitao Liu and
Mi Tian and
Ziyu Lyu and
Lixin Cui and
Zitao Liu},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{FairWork: A Generic Framework For Evaluating Fairness In LLM-Based Job Recommender System}},
booktitle = {{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025}},
pages = {3964--3968},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3730145},
doi = {https://doi.org/10.1145/3726302.3730145}
}