145 citations · 310 across the 11 of their papers we have counts for
15 papers
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…
User-centered Evaluation of Popularity Bias in Recommender Systems
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +2
Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of oth…
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…