FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback-Reference-Cited by-同舟云学术

FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Li Jie¹,Ren Yongli¹,Deng Ke¹

Affiliation:

1. School of Computing Technologies, Royal Melbourne Institute of Technology University, Australia

Funder

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3511958

Reference52 articles.

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2. Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. arXiv preprint arXiv:2006.15772(2020). Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. arXiv preprint arXiv:2006.15772(2020).

3. The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

4. Himan Abdollahpouri Masoud Mansoury Robin Burke Bamshad Mobasher and Edward Malthouse. 2021. User-centered Evaluation of Popularity Bias in Recommender Systems. arXiv preprint arXiv:2103.06364(2021). Himan Abdollahpouri Masoud Mansoury Robin Burke Bamshad Mobasher and Edward Malthouse. 2021. User-centered Evaluation of Popularity Bias in Recommender Systems. arXiv preprint arXiv:2103.06364(2021).

5. Lada A Adamic , Bernardo A Huberman , AL Barabási , R Albert , H Jeong , and G Bianconi . 2000. Power-law distribution of the world wide web. science 287, 5461 ( 2000 ), 2115–2115. Lada A Adamic, Bernardo A Huberman, AL Barabási, R Albert, H Jeong, and G Bianconi. 2000. Power-law distribution of the world wide web. science 287, 5461 (2000), 2115–2115.

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