| 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 | 47 | 2017 |
| 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 | 30 | 2020 |
| 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 | 13 | 2019 |
| 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 | 12 | 2020 |
| Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks F Eskandanian, B Mobasher RecSysKTL, ACM RecSys Workshops, 2018 | 11 | 2018 |
| Algorithmic fairness, institutional logics, and social choice R Burke, A Voida, N Mattei, N Sonboli Harvard CRCS Workshop: AI for Social Good, 2020 | 9 | 2020 |
| " 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 | 8 | 2020 |
| User Segmentation for Controlling Recommendation Diversity F Eskandanian, B Mobasher, R Burke ACM RecSys Posters, 2016 | 7 | 2016 |
| 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 | 3 | 2019 |
| Collaborative recommendation of informal learning experiences R Burke, F Eskandanian 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops …, 2016 | 1 | 2016 |
Bamshad MobasherSchool of Computing, DePaul UniversityVerified email at cs.depaul.edu
