11 citations · 16 across the 6 of their papers we have counts for
4 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…
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…
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Alessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian +4
Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally. While Large Language Models (LLMs)…
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Gustavo Escobedo, Marta Moscati, Peter Muellner +4
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes pa…