Masoud Mansoury
About
Masoud Mansoury is an Assistant Professor in Multimedia Computing Group (MMC) at Delft University of Technology (TU Delft), the Netherlands. Before joining TU Delft, he was a Postdoctoral researcher at Amsterdam Machine Learning Lab (AMLab) at University of Amsterdam and Elsevier Discovery Lab where he worked on interactive and online learning-to-rank recommendation models. Masoud obtained his PhD in Computer and Information Science from Eindhoven University of Technology under supervision of Bamshad Mobasher, Robin Burke, and Mykola Pechenizkiy. The topic of his PhD was on understanding and mitigating unfairness and algorithmic bias in recommender systems.
Masoud’s broad research interests lie in the area of Trustworthy and Explainable Recommender Systems. More specifically, he conducts research on the following topics:
-
Algorithmic bias: tackling bias issue in recommendation models to improve the business aspects and accuracy of the recommendation systems and mitigating the unfairness issue that may raise due to algorithmic bias.
-
Explainability and Transparency: understanding the logic behind the recommendation process, explaining the factors causing/leading to the recommendation outputs.
-
Robustness: detecting the malicious behavior and patterns in recommendation process to avoid unwanted manipulation of this process.
Visit my personal website for more details
Publications
-
2024
Beyond Static Calibration
The Impact of User Preference Dynamics on Calibrated Recommendation
Kun Lin / Masoud Mansoury / Farzad Eskandanian / Milad Sabouri / Bamshad Mobasher -
2024
Going Beyond Popularity and Positivity Bias
Correcting for Multifactorial Bias in Recommender Systems
Jin Huang / Harrie Oosterhuis / Masoud Mansoury / Herke Van Hoof / Maarten de Rijke -