Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems-Reference-Cited by-同舟云学术

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Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems

Published:2024-08-24 Issue: Volume:33 Page:3507-3517
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Xiang Zhichen¹^ORCID,Zhao Hongke¹^ORCID,Zhao Chuang²^ORCID,He Ming³^ORCID,Fan Jianping³^ORCID

Affiliation:

1. College of Management and Economics, Tianjin University & Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, Tianjin, China

2. The Hong Kong University of Science and Technology, Hong Kong, China

3. AI Lab at Lenovo Research, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671786

Reference55 articles.

1. Controlling Popularity Bias in Learning-to-Rank Recommendation

2. Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. arXiv preprint arXiv:1901.07555 (2019).

3. User-centered Evaluation of Popularity Bias in Recommender Systems

4. Content provider dynamics and coordination in recommendation ecosystems;Ben-Porat Omer;Advances in Neural Information Processing Systems,2020

5. Levin Brinkmann Fabian Baumann Jean-François Bonnefon Maxime Derex Thomas F Müller Anne-Marie Nussberger Agnieszka Czaplicka Alberto Acerbi Thomas L Griffiths Joseph Henrich et al. 2023. Machine culture. Nature Human Behaviour (2023) 1--14.

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