132 citations · 251 across the 6 of their papers we have counts for
6 papers
Dynamic fairness-aware recommendation through multi-agent social choice
Amanda Aird, Paresha Farastu, Joshua Sun +5
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers st…
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…
Recommender Systems and Algorithmic Hate
Jessie J. Smith, Lucia Jayne, Robin Burke
Despite increasing reliance on personalization in digital platforms, many algorithms that curate content or information for users have been met with resistance. When users feel dis…
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…
Fairness in Information Access Systems
Michael D. Ekstrand, Anubrata Das, Robin Burke +1
Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that…