I am an assistant professor at Johannes Kepler University - JKU. Prior to it, I was a post doctoral researcher at Idiap Research Institute (affiliated with EPFL), and a PhD candidate at TU Wien. I explore deep learning methods in natural language processing and information retrieval, with a focus on fairness and algorithmic bias mitigation.
Univ. Ass., Dr. Navid Rekab-saz
Assistant Professor
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), 2022
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th {ACM} Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September 2022, 2022
Investigating Gender Fairness of Recommendation Algorithms in the Music Domain
Information Processing & Management, 2021
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021
