Farzad Eskandanian
Farzad Eskandanian
Verified email at depaul.edu
Title
Cited by
Cited by
Year
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
F Eskandanian, B Mobasher, R Burke
International Conference on User Modeling, Adaptation, and Personalization, 2017
332017
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
N Sonboli, F Eskandanian, R Burke, W Liu, B Mobasher
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020
102020
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
F Eskandanian, N Sonboli, B Mobasher
The 27th Conference on User Modeling, Adaptation and Personalization (UMAP ’19), 2019
102019
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
F Eskandanian, B Mobasher
RecSysKTL, ACM RecSys Workshops, 2018
82018
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
F Eskandanian, B Mobasher
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020
52020
User Segmentation for Controlling Recommendation Diversity
F Eskandanian, B Mobasher, R Burke
ACM RecSys Posters, 2016
52016
Algorithmic fairness, institutional logics, and social choice
R Burke, A Voida, N Mattei, N Sonboli
Harvard CRCS Workshop: AI for Social Good, 2020
32020
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
F Eskandanian, B Mobasher
The 32nd International FLAIRS Conference in Association with AAAI, 2019
12019
Collaborative recommendation of informal learning experiences
R Burke, F Eskandanian
2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops …, 2016
12016
" And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
N Sonboli, R Burke, N Mattei, F Eskandanian, T Gao
arXiv preprint arXiv:2009.02590, 2020
2020
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