Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance-Reference-Cited by-同舟云学术

Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Bai Aijun¹^ORCID,Jagerman Rolf²^ORCID,Qin Zhen³^ORCID,Yan Le¹^ORCID,Kar Pratyush⁴^ORCID,Lin Bing-Rong¹^ORCID,Wang Xuanhui¹^ORCID,Bendersky Michael¹^ORCID,Najork Marc¹^ORCID

Affiliation:

1. Google LLC, Mountain View, CA, USA

2. Google LLC, Amsterdam, Netherlands

3. Google LLC, New York, NY, USA

4. Google LLC, Paris, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614712

Reference33 articles.

1. Christopher Burges , Robert Ragno , and Quoc Le. 2007. Learning to Rank with Nonsmooth Cost Functions . In Advances in Neural Information Processing Systems . MIT Press . Christopher Burges, Robert Ragno, and Quoc Le. 2007. Learning to Rank with Nonsmooth Cost Functions. In Advances in Neural Information Processing Systems. MIT Press.

2. Learning to rank using gradient descent

3. From RankNet to LambdaRank to LambdaMART;Burges Christopher JC;An Overview. Learning,2010

4. Learning to rank

5. Yahoo! learning to rank challenge overview;Chapelle Olivier;Proceedings of Machine Learning Research,2011

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