Counteracting Popularity-Bias and Improving Diversity Through Calibrated Recommendations. - Andre Sacilotti; Rodrigo Ferrari de Souza; Marcelo G. Manzato (ICEIS (1), 2023)

Enhancing Calibration and Reducing Popularity Bias in Recommender Systems. - Rodrigo Ferrari de Souza; Marcelo Garcia Manzato (ICEIS (Revised Selected Papers 2), 2023)

User Perception of Fairness-Calibrated Recommendations. - Gabrielle Alves; Dietmar Jannach; Rodrigo Ferrari de Souza; Marcelo Garcia Manzato (UMAP, 2024)

Digitally nudging users to explore off-profile recommendations: here be dragons. - Gabrielle Alves; Dietmar Jannach; Rodrigo Ferrari de Souza; Daniela E. Damian; Marcelo Garcia Manzato (User Model. User Adapt. Interact., 2024)