320 citations · 1.3k across the 27 of their papers we have counts for
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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…