145 citations · 456 across the 34 of their papers we have counts for
4 papers · 1 filter
Understanding and Mitigating Multi-Sided Exposure Bias in Recommender Systems
Masoud Mansoury
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendatio…
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…