Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study.
Zechun Niu|Zhilin Zhang|Jiaxin Mao|Qingyao Ai|Ji-Rong Wen
| Anthology ID: | DBLP:conf/sigir/NiuZMAW25 |
|---|---|
| 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: | 3265-3275 |
| URL: | https://doi.org/10.1145/3726302.3730310 |
| DOI: | https://doi.org/10.1145/3726302.3730310 |
| DBLP: | conf/sigir/NiuZMAW25 |
| BibTeX: |
@inproceedings{niu-2025-investigating,
author = {Zechun Niu and
Zhilin Zhang and
Jiaxin Mao and
Qingyao Ai and
Ji-Rong Wen},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study}},
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 = {3265--3275},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3730310},
doi = {https://doi.org/10.1145/3726302.3730310}
}