73 citations · 97 across the 3 of their papers we have counts for
3 papers
Who Pays? Personalization, Bossiness and the Cost of Fairness
Paresha Farastu, Nicholas Mattei, Robin Burke
Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or…
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher +3
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when…
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairne…