145 citations · 397 across the 15 of their papers we have counts for
28 papers
Fairness and Transparency in Recommendation: The Users' Perspective
Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus +2
Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often exp…
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…
User Factor Adaptation for User Embedding via Multitask Learning
Xiaolei Huang, Michael J. Paul, Robin Burke +2
Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentime…
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
Nasim Sonboli, Robin Burke, Nicholas Mattei +2
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairn…
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…