Unbiased Learning to Rank: On Recent Advances and Practical Applications-Reference-Cited by-同舟云学术

Unbiased Learning to Rank: On Recent Advances and Practical Applications

Published:2024-03-04 Issue: Volume: Page:
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Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Gupta Shashank¹^ORCID,Hager Philipp¹^ORCID,Huang Jin¹^ORCID,Vardasbi Ali¹^ORCID,Oosterhuis Harrie²^ORCID

Affiliation:

1. University of Amsterdam, Amsterdam, Netherlands

2. Radboud University, Nijmegen, Netherlands

Funder

Netherlands Organisa- tion for Scientific Research (NWO)

Mercury Machine Learning Lab

Dreams Lab

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3636451

Reference58 articles.

1. Aman Agarwal, Xuanhui Wang, Cheng Li, Michael Bendersky, and Marc Najork. 2019. Addressing Trust Bias for Unbiased Learning-to-rank. In The World Wide Web Conference. 4--14.

2. Qingyao Ai, Jiaxin Mao, Yiqun Liu, and W Bruce Croft. 2018. Unbiased learning to rank: Theory and practice. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2305--2306.

3. Asia J Biega, Krishna P Gummadi, and GerhardWeikum. 2018. Equity of attention: Amortizing individual fairness in rankings. In The 41st international acm sigir conference on research & development in information retrieval. 405--414.

4. Adam Block, Rahul Kidambi, Daniel N Hill, Thorsten Joachims, and Inderjit S Dhillon. 2022. Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. arXiv preprint arXiv:2204.10936 (2022).

5. Olivier Chapelle and Yi Chang. 2011. Yahoo! Learning to Rank Challenge Overview. In Proceedings of the learning to rank challenge. PMLR, 1--24.

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