20 citations · 39 across the 4 of their papers we have counts for
6 papers
"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…
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
Farzad Eskandanian, Bamshad Mobasher
Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias…
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
Nasim Sonboli, Farzad Eskandanian, Robin Burke +2
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fa…
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…
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
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. User preferences on items tend to chang…
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
Farzad Eskandanian, Bamshad Mobasher
Recommender systems help users find relevant items of interest based on the past preferences of those users. In many domains, however, the tastes and preferences of users change ov…