Alessandro B. Melchiorre, David Penz, Christian Ganhör, Oleg Lesota, Vasco Bezold Rosner Fragoso, Florian Friztl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl
Emotion-aware Music Tower Blocks (EmoMTB): An Intelligent Audiovisual Interface for Music Discovery and Recommendation
International Journal of Multimedia Information Retrieval (IJMIR) To Appear, 2023

Christian Ganhör, David Penz, Navid Rekab-saz, Oleg Lesota, Markus Schedl
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022

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, 2022

Alessandro B. Melchiorre, David Penz, Christian Ganhör, Oleg Lesota, Vasco Bezold Rosner Fragoso, Florian Friztl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl
EmoMTB: Emotion-aware Music Tower Blocks
Proceedings of the 2022 ACM International Conference on Multimedia Retrieval (ICMR), 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

Oleg Lesota, Navid Rekab-saz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021