Paper-Conference

Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz
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Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz
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A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex
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Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl
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Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval Results
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz
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Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning
Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekabsaz, Markus Schedl
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Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl
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Music Emotions in Solo Piano: Bridging the Gap Between Human Perception and Machine Learning
Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Björn Schuller, Markus Schedl
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Sequential Recommendation Models: A Graph-based Perspective
Andreas Peintner
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Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine Users
Simone Kopeinik, Martina Mara, Linda Ratz, Klara Krieg, Markus Schedl, Navid Rekabsaz
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