145 citations · 356 across the 26 of their papers we have counts for
8 papers · 1 filter
The Impact of Popularity Bias on Fairness and Calibration 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.…
Crank up the volume: preference bias amplification in collaborative recommendation
Kun Lin, Nasim Sonboli, Bamshad Mobasher +1
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration,…
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
Masoud Mansoury, Bamshad Mobasher, Robin Burke +1
Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a gro…
Flatter is better: Percentile Transformations for Recommender Systems
Masoud Mansoury, Robin Burke, Bamshad Mobasher
It is well known that explicit user ratings in recommender systems are biased towards high ratings, and that users differ significantly in their usage of the rating scale. Implemen…
The Unfairness of Popularity Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented…
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overal…