Paper-Conference

Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models

Gustavo Escobedo, Marta Moscati, Peter Müllner, Simone Kopeinik, Dominik Kowald, Elisabeth Lex, Markus Schedl

Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems

Alessandro B. Melchiorre, Shahed Masoudian, Deepak Kumar, Markus Schedl

Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training

Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl

Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters

Shahed Masoudian, Volaucnik Cornelia, Markus Schedl, Navid Rekab-Saz

A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios

Christian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, Markus Schedl

Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags

Marta Moscati, Hannah Strauß, Peer-Ole Jacobsen, Andreas Peintner, Eva Zangerle, Marcel Zentner, Markus Schedl

Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning

Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential …

Pavan Seshadri, Shahrzad Shashaani, Peter Knees

Explainability in Music Recommender System

Recommendation systems play a crucial role in our daily lives, influencing many of our significant and minor decisions. These systems …

Shahrzad Shashaani

Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research

Moritz Staudinger, Bettina M. J. Kern, Tomasz Miksa, Lukas Arnhold, Peter Knees, Andreas Rauber, Allan Hanbury

Mosaikbox: Improving Fully Automatic DJ Mixing Through Rule-based Stem Modification And Precise Beat-Grid Estimation

Robert Sowula, Peter Knees