no code implementations • 22 Aug 2022 • Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi, Nasim Sonboli
Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data.
no code implementations • 16 Mar 2021 • Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler
Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation.
no code implementations • 5 Sep 2020 • Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao
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 fairness these systems exhibit.
no code implementations • 21 May 2020 • Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results.
no code implementations • 13 Mar 2020 • Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society.
no code implementations • 13 Sep 2019 • Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences.
no code implementations • 14 May 2019 • 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 overall behavior of the system.
1 code implementation • 12 Sep 2018 • Robin Burke, Jackson Kontny, Nasim Sonboli
When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets.
Computers and Society