Fairness and discrimination in recommendation and retrieval-Reference-Cited by-同舟云学术

Fairness and discrimination in recommendation and retrieval

Published:2019-09-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th ACM Conference on Recommender Systems
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Author:

Ekstrand Michael D¹,Burke Robin²,Diaz Fernando³

Affiliation:

1. Boise State University

2. University of Colorado

3. Microsoft Research, Montréal, Quebec

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3298689.3346964

Reference15 articles.

1. Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof and Ed H. Chi. 2019. Putting Fairness Principles into Practice: Challenges Metrics and Improvements. CoRR abs/1901.04562 (2019). Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof and Ed H. Chi. 2019. Putting Fairness Principles into Practice: Challenges Metrics and Improvements. CoRR abs/1901.04562 (2019).

2. Equity of Attention

3. Robin Burke. 2017. Multisided Fairness for Recommendation. (July 2017). arXiv:cs.CY/1707.00093 http://arxiv.org/abs/1707.00093 Robin Burke. 2017. Multisided Fairness for Recommendation. (July 2017). arXiv:cs.CY/1707.00093 http://arxiv.org/abs/1707.00093

4. Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. (Oct. 2018). arXiv:cs.LG/1810.08810 http://arxiv.org/abs/1810.08810 Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. (Oct. 2018). arXiv:cs.LG/1810.08810 http://arxiv.org/abs/1810.08810

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