145 citations · 456 across the 34 of their papers we have counts for
7 papers · 1 filter
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…
Multi-sided Exposure Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury
Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, div…
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…
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…
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Jessie Smith +3
The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and…