145 citations · 456 across the 34 of their papers we have counts for
4 papers · 1 filter
Correcting Popularity Bias in Recommender Systems via Item Loss Equalization
Juno Prent, Masoud Mansoury
Recommender Systems (RS) often suffer from popularity bias, where a small set of popular items dominate the recommendation results due to their high interaction rates, leaving many…
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly probl…
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Kun Lin, Masoud Mansoury, Farzad Eskandanian +2
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…