320 citations · 1.3k across the 27 of their papers we have counts for
9 papers · 1 filter
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
Nasim Sonboli, Robin Burke, Nicholas Mattei +2
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairn…
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A…
Feedback Loop and Bias Amplification in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendati…
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effe…
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
Nasim Sonboli, Farzad Eskandanian, Robin Burke +2
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fa…
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
Recommender systems are often biased toward popular items. In other words, few items are frequently recommended while the majority of items do not get proportionate attention. That…
Exploring User Opinions of Fairness in Recommender Systems
Jessie Smith, Nasim Sonboli, Casey Fiesler +1
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents…
Developing a Recommendation Benchmark for MLPerf Training and Inference
Carole-Jean Wu, Robin Burke, Ed H. Chi +4
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enh…
Unfair Exposure of Artists in Music Recommendation
Himan Abdollahpouri, Robin Burke, Masoud Mansoury
Fairness in machine learning has been studied by many researchers. In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain c…