Eva Zangerle

Ass.-Prof. Priv.-Doz. Dr. Eva Zangerle

Tel: +43 512 507 53236
Office
ICT building, 2nd floor, room 3W02
Consultation Hours
schedule meeting here

Eva is an assistant professor at the Department of Computer Science at the University of Innsbruck, Austria. She recently was awarded the habilitation degree (venia docendi) in Computer Science in 2023 (title of thesis: "Recommender Systems for Music Retrieval Tasks"). In 2013, Eva earned her Ph.D. from the University of Innsbruck, focusing on recommender systems for collaborative social media platforms. She has expanded her research horizons by undertaking short-term research stays at Ritsumeikan University in Kyoto, Japan, Freie Universität Berlin, Germany, and Johannes Kepler University Linz, Austria.

Eva's primary scientific interests revolve around the field of recommender systems, particularly their evaluation and user modeling, particularly within the domain of music information retrieval. Eva has been honored with the Women in RecSys Best Journal Paper of the Year award in both 2022 and 2023. Eva is one of the organizers of the PERSPECTIVES workshop series "Perspectives on the Evaluation of Recommender Systems".

In addition to her scholarly endeavors, Eva is also a co-author of a book on MySQL, which is currently in its third edition. 

Publications

2024

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Axel Soto and Eva Zangerle: Joint Proceedings of the ACM IUI 2024 Workshops co-located with the 29th Annual ACM Conference on Intelligent User Interfaces (IUI 2024), Greenville, South Carolina, USA, March 18, 2024.  Vol. 3660. CEUR-WS.org, 2024

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Emilia Parada-Cabaleiro, Maximilian Mayerl, Stefan Brandl, Marcin Skowron, Markus Schedl, Elisabeth Lex and Eva Zangerle: Song lyrics have become simpler and more repetitive over the last five decades. In Scientific Reports, vol. 14, no. 1, pages 5531. Nature Publishing Group UK London, 2024

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Janek Bevendorff, Xavier Bonet Casals, Berta Chulvi, Daryna Dementieva, Ashaf Elnagar, Dayne Freitag, Maik Fröbe, Damir Korencic, Maximilian Mayerl, Animesh Mukherjee, Alexander Panchenko, Martin Potthast, Francisco Rangel, Paolo Rosso, Alisa Smirnova, Efstathios Stamatatos, Benno Stein, Mariona Taule, Dmitry Ustalov, Matti Wiegmann and Eva Zangerle: Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification - Extended Abstract. In Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part VI, vol. 14613, pages 3-10. Springer, 2024

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Christine Bauer, Alan Said and Eva Zangerle: Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation. In ACM Transactions on Recommender Systems, vol. 2, no. 1. Association for Computing Machinery, 2024

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Christine Bauer, Eva Zangerle and Alan Said: Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives. In ACM Transactions on Recommender Systems, vol. 2, no. 1. Association for Computing Machinery, 2024

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Alan Said, Eva Zangerle and Christine Bauer: Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023. In SIGIR Forum, vol. 57, no. 2. Association for Computing Machinery, 2024

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Hannah Strauss, Julia Vigl, Peer-Ole Jacobsen, Martin Bayer, Francesca Talamini, Wolfgang Vigl, Eva Zangerle and Marcel Zentner: The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts. In Behavior Research Methods, pages 1-18. Springer, 2024

2023

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Eva Zangerle, Maximilian Mayerl, Martin Potthast and Benno Stein: Overview of the Multi-Author Writing Style Analysis Task at PAN 2023. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023), Thessaloniki, Greece, September 18th to 21st, 2023, vol. 3497, pages 2513-2522. CEUR-WS.org, 2023

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Janek Bevendorff, Ian Borrego-Obrador, Mara Chinea-Rios, Marc Franco-Salvador, Maik Fröbe, Annina Heini, Krzysztof Kredens, Maximilian Mayerl, Piotr Pkezik, Martin Potthast, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, Benno Stein, Matti Wiegmann, Magdalena Wolska and Eva Zangerle: Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection. In Experimental IR Meets Multilinguality, Multimodality, and Interaction, pages 459-481. Springer Nature Switzerland, 2023

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Andreas Peintner, Amir Reza Mohammadi and Eva Zangerle: SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 58-69. ACM, 2023

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Alan Said, Eva Zangerle, and Christine Bauer: Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, September 19, 2023. Vol. 3476. CEUR-WS.org, 2023

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Alan Said, Eva Zangerle and Christine Bauer: Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023). In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 1221-1222. ACM, 2023

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Eva Zangerle and Christine Bauer: Evaluating Recommender Systems: Survey and Framework. In ACM Computing Surveys, vol. 55, no. 8. Association for Computing Machinery, 2023

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Michael Vötter, Maximilian Mayerl, Eva Zangerle and Günther Specht: Song Popularity Prediction using Ordinal Classification. In Proceedings of the 20th Sound and Music Computing Conference. June 15-17, 2023. Stockholm, Sweden. Royal College of Music and KTH Royal Institute of Technology, 2023

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Maximilian Mayerl, Michael Vötter, Günther Specht and Eva Zangerle: Pairwise Learning to Rank for Hit Song Prediction. In BTW 2023. Gesellschaft für Informatik e.V., 2023

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Manfred Moosleitner, Günther Specht and Eva Zangerle: Detection of Generated Text Reviews by Leveraging Methods from Authorship Attribution: Predictive Performance vs. Resourcefulness. In BTW 2023. Gesellschaft für Informatik e.V., 2023

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Eva Zangerle, Christine Bauer and Alan Said: Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022. In SIGIR Forum, vol. 56, no. 2. Association for Computing Machinery, 2023.

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Janek Bevendorff, Mara Chinea-Rios, Marc Franco-Salvador, Annina Heini, Erik Körner, Krzysztof Kredens, Maximilian Mayerl, Piotr Pkezik, Martin Potthast, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, Benno Stein, Matti Wiegmann, Magdalena Wolska and Eva Zangerle: Overview of PAN 2023: Authorship Verification, Multi-author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection. In Advances in Information Retrieval (ECIR 2023), pages 518-526. Springer Nature Switzerland, 2023

2022

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Eva Zangerle: Recommender Systems for Music Retrieval Tasks. Habilitation Thesis, University of Innsbruck, 2022

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Maximilian Mayerl, Stefan Brandl, Günther Specht, Markus Schedl and Eva Zangerle: Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition. In Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022, pages 884-890. ISMIR, 2022