TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference.
Zuoli Tang|Zhaoxin Huan|Zihao Li|Shirui Hu|Xiaolu Zhang|Jun Zhou|Lixin Zou|Chenliang Li
| Anthology ID: | DBLP:conf/cikm/TangHLHZ0ZL24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2240-2249 |
| URL: | https://doi.org/10.1145/3627673.3679581 |
| DOI: | https://doi.org/10.1145/3627673.3679581 |
| DBLP: | conf/cikm/TangHLHZ0ZL24 |
| BibTeX: |
@inproceedings{tang-2024-text,
author = {Zuoli Tang and
Zhaoxin Huan and
Zihao Li and
Shirui Hu and
Xiaolu Zhang and
Jun Zhou and
Lixin Zou and
Chenliang Li},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {2240--2249},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679581},
doi = {https://doi.org/10.1145/3627673.3679581}
}