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} }