Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair Ranking-Reference-Cited by-同舟云学术

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Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair Ranking

Published:2023-08-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval
language:
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Author:

Chen Fumian¹^ORCID,Fang Hui¹^ORCID

Affiliation:

1. University of Delaware, Newark, DE, USA

Funder

Institute for Financial Services Analytics at University of Delaware

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3578337.3605132

Reference38 articles.

1. Gabriel Bénédict Ali Vardasbi , Shashank Gupta , Maria Heuss , Pooya Khandel , Ming Li , and Fatemeh Sarvi . 2021 . The University of Amsterdam at the TREC 2021 Fair Ranking Track . In Proceedings of TREC. Gabriel Bénédict Ali Vardasbi, Shashank Gupta, Maria Heuss, Pooya Khandel, Ming Li, and Fatemeh Sarvi. 2021. The University of Amsterdam at the TREC 2021 Fair Ranking Track. In Proceedings of TREC.

2. From ranknet to lambdarank to lambdamart: An overview;Burges Christopher JC;Learning,2010

3. Learning to rank

4. The use of MMR, diversity-based reranking for reordering documents and producing summaries

5. Controlling Polarization in Personalization

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