Joint Multisided Exposure Fairness for Recommendation-Reference-Cited by-同舟云学术

Joint Multisided Exposure Fairness for Recommendation

Published:2022-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wu Haolun¹,Mitra Bhaskar²,Ma Chen³,Diaz Fernando⁴,Liu Xue¹

Affiliation:

1. McGill University, Montréal, PQ, Canada

2. Microsoft, Montréal, PQ, Canada

3. City University of Hong Kong, Hong Kong SAR, Hong Kong

4. Canadian CIFAR AI Chair & Google, Montréal, PQ, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3532007

Reference65 articles.

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2. Ruha Benjamin . 2019. Race after technology: Abolitionist tools for the new jim code. Social Forces ( 2019 ). Ruha Benjamin. 2019. Race after technology: Abolitionist tools for the new jim code. Social Forces (2019).

3. Equity of Attention

4. Avishek Joey Bose and William L . Hamilton . 2019 . Compositional Fairness Constraints for Graph Embeddings. In ICML (Proceedings of Machine Learning Research , Vol. 97). PMLR, 715-- 724 . Avishek Joey Bose and William L. Hamilton. 2019. Compositional Fairness Constraints for Graph Embeddings. In ICML (Proceedings of Machine Learning Research, Vol. 97). PMLR, 715--724.

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