Publications

Alessandro B. Melchiorre, Shahed Masoudian, Deepak Kumar, Markus Schedl (2024). Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024).

Gustavo Escobedo, Marta Moscati, Peter Müllner, Simone Kopeinik, Dominik Kowald, Elisabeth Lex, Markus Schedl (2024). Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024).

Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl (2024). 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 at SIGIR 2024).

Christine Bauer, Alan Said, Eva Zangerle (2024). Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation. ACM Transactions on Recommender Systems.

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Shahed Masoudian, Volaucnik Cornelia, Markus Schedl, Navid Rekab-saz (2024). Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters. The 18th Conference of the European Chapter of the Association for Computational Linguistics March 17-22, 2024.

Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl (2024). Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

Markus Schedl, Vito Walter Anelli, Elisabeth Lex (2024). Trustworthy User Modeling and Recommendation From Technical and Regulatory Perspectives. Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization.

Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald (2024). The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias. Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part IV.

Hannah Strauss, Julia Vigl, Peer-Ole Jacobsen, Martin Bayer, Francesca Talamini, Wolfgang Vigl, Eva Zangerle, Marcel Zentner (2024). The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts. Behavior Research Methods.

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

Markus Frohmann, Igor Sterner, Ivan Vulić, Benjamin Minixhofer, Markus Schedl (2024). Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

Alan Said, Eva Zangerle, Christine Bauer (2024). Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023. SIGIR Forum.

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Markus Schedl, Marta Moscati, Bruno Sguerra, Romain Hennequin, Elisabeth Lex (2024). Psychology-informed Information Access Systems Workshop. Proceedings of the 17th ACM International Conference on Web Search and Data Mining.

Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl (2024). Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. Proceedings of the 18th ACM Conference on Recommender Systems.

Marta Moscati, Hannah Strauß, Peer-Ole Jacobsen, Andreas Peintner, Eva Zangerle, Marcel Zentner, Markus Schedl (2024). Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization.

Yashar Deldjoo, Markus Schedl, Peter Knees (2024). Content-driven music recommendation: Evolution, state of the art, and challenges. Computer Science Review.

Christian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, Markus Schedl (2024). A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios. Proceedings of the 18th ACM Conference on Recommender Systems.

Christine Bauer, Eva Zangerle, Alan Said (2023). Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives. ACM Trans. Recomm. Syst..

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Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz (2023). Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. Findings of the Association for Computational Linguistics: ACL 2023.

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Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz (2023). Parameter-efficient Modularised Bias Mitigation via AdapterFusion. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.

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Markus Schedl, Vito Walter Anelli, Elisabeth Lex (2023). Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives. Proceedings of the 17th ACM Conference on Recommender Systems.

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Markus Schedl, Emilia Gómez, Elisabeth Lex (2023). Trustworthy Algorithmic Ranking Systems. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining.

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Andreas Peintner, Amir Reza Mohammadi, Eva Zangerle (2023). SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023.

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Simone Kopeinik, Martina Mara, Linda Ratz, Klara Krieg, Markus Schedl, Navid Rekabsaz (2023). Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine Users. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.

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Andreas Peintner (2023). Sequential Recommendation Models: A Graph-based Perspective. Proceedings of the 17th ACM Conference on Recommender Systems.

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Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Markus Schedl, Giovanni Constantini, Björn Schuller (2023). Perception and classification of emotions in nonsense speech: Humans versus machines. PLOS ONE.

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Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Björn Schuller, Markus Schedl (2023). Music Emotions in Solo Piano: Bridging the Gap Between Human Perception and Machine Learning. Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research (CMMR 2023), Tokyo, Japan, November 2023.

Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl (2023). Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. Proceedings of the 17th ACM Conference on Recommender Systems.

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Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekabsaz, Markus Schedl (2023). Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning. Proceedings of the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023) co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, 18th-22nd September 2023.

Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz (2023). Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval Results. Proceedings of the 2023 Conference on Human Information Interaction and Retrieval.

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Deepak Kumar, Tessa Grosz, Navid Rekabsaz, Elisabeth Greif, Markus Schedl (2023). Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives. Frontiers in Big Data.

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Emilia Parada-Cabaleiro, Anton Batliner, Marcel Zentner, Markus Schedl (2023). Exploring emotions in Bach chorales: a multi-modal perceptual and data-driven study. Royal Society Open Science.

Alessandro B Melchiorre, David Penz, Christian Ganhör, Oleg Lesota, Vasco Fragoso, Florian Fritzl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl (2023). Emotion-aware music tower blocks (EmoMTB): an intelligent audiovisual interface for music discovery and recommendation. International Journal of Multimedia Information Retrieval.

Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald (2023). Differential privacy in collaborative filtering recommender systems: a review. Frontiers in Big Data.

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Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl (2023). Computational Versus Perceived Popularity Miscalibration in Recommender Systems. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.

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Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex (2023). A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. Advances in Bias and Fairness in Information Retrieval.

Maximilian Mayerl, Stefan Brandl, Günther Specht, Markus Schedl, Eva Zangerle (2022). Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition. Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022 (ISMIR 2022).

Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekabsaz, Markus Schedl (2022). Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms. Proceedings of the 23st International Society for Music Information Retrieval Conference 2022 (ISMIR 2022).

Maximilian Damböck, Richard Vogl, Peter Knees (2022). On the Impact and Interplay of Input Representations and Network Architectures for Automatic Music Tagging. Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022 (ISMIR 2022).

Andreas Peintner, Marta Moscati, Emilia Parada-Cabaleiro, Markus Schedl, Eva Zangerle (2022). Unsupervised Graph Embeddings for Session-based Recommendation with Item Features. CARS - Workshop on Context-Aware Recommender Systems.

Peter Knees, Andres Ferraro, Moritz Hübler (2022). Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact. 2nd Workshop on Multi-Objective Recommender Systems (MORS@RecSys2022).

Eva Zangerle, Christine Bauer (2022). Evaluating Recommender Systems: Survey and Framework. ACM Comput. Surv..

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Markus Schedl, Stefan Brandl, Oleg Lesota, Emilia Parada-Cabaleiro, David Penz, Navid Rekabsaz (2022). LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. ACM SIGIR Conference on Human Information Interaction and Retrieval.

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Damjan Prvulovic, Richard Vogl, and Peter Knees (2022). ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors. Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP'22 Adjunct).

Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl (2022). Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation Dataset. Proceedings of the 31st ACM International Conference on Information & Knowledge Management.

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Emilia Parada-Cabaleiro, Anton Batliner, Markus Schedl (2022). An Exploratory Study on the Acoustic Musical Properties to Decrease Self-Perceived Anxiety. International Journal of Environmental Research and Public Health.

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Peter Knees, Bruce Ferwerda, Andreas Rauber, Sebastian Strumbelj, Annabel Resch, Laurenz Tomandl, Valentin Bauer, Tang Fung Yee, Josip Bobinac, Amila Ceranic, Riad Dizdar (2022). A Reproducibility Study on User-centric MIR Research and Why it is Important. Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022 (ISMIR 2022).

Tomislav Duricic, Dominik Kowald, Markus Schedl, Elisabeth Lex (2021). My friends also prefer diverse music. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

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Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex (2021). Predicting Music Relistening Behavior Using the ACT-R Framework. Fifteenth ACM Conference on Recommender Systems.

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Harald Schweiger, Emilia Parada-Cabaleiro, Markus Schedl (2021). Does Track Sequence in User-generated Playlists Matter?. Proceedings of the 22nd International Society for Music Information Retrieval Conference 2021 (ISMIR 2021).