User Segmentation for Controlling Recommendation Diversity. - Farzad Eskandanian; Bamshad Mobasher; Robin D. Burke (RecSys Posters, 2016)

Collaborative Recommendation of Informal Learning Experiences. - Robin D. Burke; Farzad Eskandanian (WI Workshops, 2016)

A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. - Farzad Eskandanian; Bamshad Mobasher; Robin Burke (UMAP, 2017)

Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks. - Farzad Eskandanian; Bamshad Mobasher (CoRR, 2018)

Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. - Farzad Eskandanian; Bamshad Mobasher (FLAIRS, 2019)

Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. - Farzad Eskandanian; Nasim Sonboli; Bamshad Mobasher (UMAP, 2019)

Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. - Farzad Eskandanian; Bamshad Mobasher (UMAP, 2020)

Opportunistic Multi-aspect Fairness through Personalized Re-ranking. - Nasim Sonboli; Farzad Eskandanian; Robin Burke; Weiwen Liu; Bamshad Mobasher (UMAP, 2020)

"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation. - Nasim Sonboli; Robin Burke; Nicholas Mattei; Farzad Eskandanian; Tian Gao (CoRR, 2020)

Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. - Kun Lin; Masoud Mansoury; Farzad Eskandanian; Milad Sabouri; Bamshad Mobasher (UMAP (Adjunct Publication), 2024)