11 citations · 27 across the 12 of their papers we have counts for
3 papers · 1 filter
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…
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
Peter Müllner, Dominik Kowald, Markus Schedl +1
Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades accuracy. We address this tra…
Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification
Nathanya Satriani, Djordje Slijepčević, Markus Schedl +1
Explanatory interactive learning (XIL) enables users to guide model training in machine learning (ML) by providing feedback on the model's explanations, thereby helping it to focus…