Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
Proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation (BIAS @ SIGIR 2024), 2024

Emilia Parada-Cabaleiro, Maximilian Mayerl, Stefan Brandl, Marcin Skowron, Markus Schedl, Elisabeth Lex, Eva Zangerle
Song lyrics have become simpler and more repetitive over the last five decades
Scientific Reports, 2024

Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September, 2022

Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms
Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR 2022, Bengaluru, India, December 4-8, 2022

Alessandro B. Melchiorre, Navid Rekab-saz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, Markus Schedl
Investigating Gender Fairness of Recommendation Algorithms in the Music Domain
Information Processing & Management, 2021

Oleg Lesota, Alessandro B. Melchiorre, Navid Rekab-saz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021