Search Results for author: Milad Sabouri

Found 2 papers, 1 papers with code

Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation

1 code implementation • 16 May 2024 • Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher

Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system.

Recommendation Systems

Paper
Code

Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation

no code implementations • 7 Aug 2021 • Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher, Mykola Pechenizkiy, Robin Burke, Milad Sabouri

This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists.

Exposure Fairness Learning-To-Rank +1

Paper
Add Code

Cannot find the paper you are looking for? You can Submit a new open access paper.