CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Author:

Naghiaei Mohammadmehdi1,Rahmani Hossein A.2,Deldjoo Yashar3

Affiliation:

1. University of Southern California, California, CA, USA

2. University College London, London, United Kingdom

3. Polytechnic University of Bari, Bari, Italy

Publisher

ACM

Reference40 articles.

1. Multistakeholder recommendation: Survey and research directions

2. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019 a. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference . Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019 a. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference .

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

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

5. Reuben Binns . 2018 . Fairness in machine learning: Lessons from political philosophy . In Conference on Fairness, Accountability and Transparency. PMLR, 149--159 . Reuben Binns. 2018. Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency. PMLR, 149--159.

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