1 code implementation • 16 May 2024 • Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system.
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 • 5 Jun 2020 • Farzad Eskandanian, Bamshad Mobasher
In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.
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 • 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.
no code implementations • 14 May 2019 • Farzad Eskandanian, Bamshad Mobasher
In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences.
no code implementations • 29 Sep 2018 • Farzad Eskandanian, Bamshad Mobasher
In the second approach the HMM is used directly to generate recommendations taking into account the identified change points.