Can LLMs Enhance Fairness in Recommendation Systems? A Data Augmentation Approach.
Hanzhe Li|Dazhong Shen|Chao Wang|Yuting Liu|Jingjing Gu
| Anthology ID: | DBLP:conf/sigir/0001S00G25 |
|---|---|
| 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: | 570-580 |
| URL: | https://doi.org/10.1145/3726302.3729917 |
| DOI: | https://doi.org/10.1145/3726302.3729917 |
| DBLP: | conf/sigir/0001S00G25 |
| BibTeX: |
@inproceedings{li-2025-llms,
author = {Hanzhe Li and
Dazhong Shen and
Chao Wang and
Yuting Liu and
Jingjing Gu},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{Can LLMs Enhance Fairness in Recommendation Systems? A Data Augmentation Approach}},
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 = {570--580},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3729917},
doi = {https://doi.org/10.1145/3726302.3729917}
}