P-MMF: Provider Max-min Fairness Re-ranking in Recommender System-Reference-Cited by-同舟云学术

P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Xu Chen¹^ORCID,Chen Sirui²^ORCID,Xu Jun¹^ORCID,Shen Weiran¹^ORCID,Zhang Xiao¹^ORCID,Wang Gang³^ORCID,Dong Zhenhua³^ORCID

Affiliation:

1. Gaoling School of Artificial Intelligence, Renmin University of China, China

2. School of Information, Renmin University of China, China

3. Huawei Noah's ark lab, Huawei Technologies, China

Funder

National Natural Science Foundation of China

Outstanding Innovative Talents Cultivation Funded Programs 2023 of Renmin University of China

Intelligent Social Governance Interdisciplinary Platform, Major Innovation \& Planning Interdisciplinary Platform for the ``Double-First Class' Initiative

National Key R\&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583296

Reference52 articles.

1. Multistakeholder recommendation: Survey and research directions

2. Himan Abdollahpouri and Robin Burke . 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158 ( 2019 ). Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158 (2019).

3. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 ( 2019 ). Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).

4. Akshay Agrawal , Brandon Amos , Shane Barratt , Stephen Boyd , Steven Diamond , and J Zico Kolter . 2019. Differentiable convex optimization layers. Advances in neural information processing systems 32 ( 2019 ). Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter. 2019. Differentiable convex optimization layers. Advances in neural information processing systems 32 (2019).

5. Santiago Balseiro , Haihao Lu , and Vahab Mirrokni . 2021 . Regularized online allocation problems: Fairness and beyond . In International Conference on Machine Learning. PMLR, 630–639 . Santiago Balseiro, Haihao Lu, and Vahab Mirrokni. 2021. Regularized online allocation problems: Fairness and beyond. In International Conference on Machine Learning. PMLR, 630–639.

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