15 citations · 22 across the 7 of their papers we have counts for
7 papers
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…
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…
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis
Masoud Mansoury, Finn Duijvestijn, Imane Mourabet
Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-re…
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury +2
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, ca…
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
Spyros Avlonitis, Dor Lavi, Masoud Mansoury +1
This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the…
Fairness of Exposure in Dynamic Recommendation
Masoud Mansoury, Bamshad Mobasher
Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic wh…