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Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored.
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Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 383–390
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Debiasing the human-recommender system feedback loop in collaborative filtering. In Companion Proceedings of The 2019 World Wide Web Conference . 645–651
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Multi-sided Exposure Bias in Recommendation. In KDD Workshop on Industrial Recommendation Systems
Himan Abdollahpouri and Masoud Mansoury. 2020 · 2020
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How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM Conference on Recommender Systems . 224–232
Allison JB Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018 · 2018
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Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 525–534
Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, and Yoni Halpern. 2020 · 2020
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FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization . 154–162
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020 · 2020
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