68 citations · 133 across the 10 of their papers we have counts for
12 papers · 1 filter
What Price Fairness? Evaluating Energy - Fairness - Accuracy Trade-off in Recommender Systems
Abhirup Mitra, Oleg Lesota, Antonela Tommasel
Fairness-aware recommender systems aim to mitigate systematic imbalances in recommendation outcomes, including how visibility, relevance, and opportunities are distributed among us…
Training seeds and model-selection stability in recommender-system evaluation
Juan Manuel Rodriguez, Oleg Lesota, Antonela Tommasel
Recommender-system experiments often rely on a single random training seed, assuming that run-to-run stochasticity has limited impact on evaluation conclusions. This assumption is…
Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study
Oleg Lesota, Gustavo Escobedo, Bruce Ferwerda +4
Popularity calibration in recommender systems has been studied both as a form of user-centered personalization and as an indicator of popularity bias. Most existing work evaluates…
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Zihan Li, Gustavo Escobedo, Marta Moscati +2
Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we intr…
The Importance of Cognitive Biases in the Recommendation Ecosystem
Markus Schedl, Oleg Lesota, Stefan Brandl +3
Cognitive biases have been studied in psychology, sociology, and behavioral economics for decades. Traditionally, they have been considered a negative human trait that leads to inf…
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
Oleg Lesota, Jonas Geiger, Max Walder +2
Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training dat…