Farzad Eskandanian

15 papers A 4Misc 1Journal 7Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. UMAP (Adjunct Publication) Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
2024	J	`jnl`	Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. CoRR Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher
2020	J	`jnl`	"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation. CoRR Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao
2020	A	`conf`	Opportunistic Multi-aspect Fairness through Personalized Re-ranking. UMAP Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
2020	J	`jnl`	Opportunistic Multi-aspect Fairness through Personalized Re-ranking. CoRR Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
2020	A	`conf`	Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. UMAP Farzad Eskandanian, Bamshad Mobasher
2020	J	`jnl`	Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. CoRR Farzad Eskandanian, Bamshad Mobasher
2019	Misc	`conf`	Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. FLAIRS Farzad Eskandanian, Bamshad Mobasher
2019	J	`jnl`	Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. CoRR Farzad Eskandanian, Bamshad Mobasher
2019	A	`conf`	Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. UMAP Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
2019	J	`jnl`	Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. CoRR Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
2018	J	`jnl`	Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks. CoRR Farzad Eskandanian, Bamshad Mobasher
2017	A	`conf`	A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. UMAP Farzad Eskandanian, Bamshad Mobasher, Robin Burke
2016	—	`conf`	Collaborative Recommendation of Informal Learning Experiences. WI Workshops Robin D. Burke, Farzad Eskandanian
2016	—	`conf`	User Segmentation for Controlling Recommendation Diversity. RecSys Posters Farzad Eskandanian, Bamshad Mobasher, Robin D. Burke