Eva Zangerle

Assoc. Prof. Dr. Eva Zangerle

eva.zangerle [at] uibk.ac.at
Scientific Staff
Tel: +43 512 507 53236
Office
ICT building, 2nd floor, room 3W02
Consultation Hours
schedule meeting here

Eva is an associate professor at the Department of Computer Science at the University of Innsbruck, Austria. She 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

2021

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Janek Bevendorff, Berta Chulvi, Gretel Liz De la Pena Sarracen, Mike Kestemont, Enrique Manjavacas, Ilia Markov, Maximilian Mayerl, Martin Potthast, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, Benno Stein, Matti Wiegmann, Magdalena Wolska and Eva Zangerle: Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection. In Experimental IR Meets Multilinguality, Multimodality, and Interaction - 12th International Conference of the CLEF Association, CLEF 2021, Proceedings, vol. 12880, pages 419-431. Springer, 2021

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Eva Zangerle, Christine Bauer and Alan Said: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES). In Fifteenth ACM Conference on Recommender Systems, pages 794–795. Association for Computing Machinery, 2021

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Martin Pichl and Eva Zangerle: User models for multi-context-aware music recommendation. In Multimedia Tools and Applications, vol. 80, no. 15, pages 22509-22531. Springer, 2021

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Janek Bevendorff, BERTa Chulvi, Gretel Liz De La Pe\~na Sarrac\'en, Mike Kestemont, Enrique Manjavacas, Ilia Markov, Maximilian Mayerl, Martin Potthast, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, Benno Stein, Matti Wiegmann, Magdalena Wolska and Eva Zangerle: Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection. In Advances in Information Retrieval. ECIR 2021, pages 567-573. Springer International Publishing, 2021

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Eva Zangerle, Chih-Ming Chen, Ming-Feng Tsai and Yi-Hsuan Yang: Leveraging Affective Hashtags for Ranking Music Recommendations. In IEEE Transactions on Affective Computing, vol. 12, no. 1, pages 78-91. 2021

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Dominik Kowald, Peter Muellner, Eva Zangerle, Christine Bauer, Markus Schedl and Elisabeth Lex: Support the underground: characteristics of beyond-mainstream music listeners. In EPJ Data Science, vol. 10, no. 1, pages 1-26. Springer, 2021

2020

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Julie Cumming, Jin Ha Lee, Brian McFee, Markus Schedl, Johanna Devaney, Cory McKay, Eva Zangerle and Timothy de Reuse: Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020, Montreal, Canada, October 11-16, 2020. 

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Janek Bevendorff, Bilal Ghanem, Anastasia Giachanou, Mike Kestemont, Enrique Manjavacas, Ilia Markov, Maximilian Mayerl, Martin Potthast, Francisco Rangel, Paolo Rosso, Günther Specht, Efstathios Stamatatos, Benno~Stein, Matti Wiegmann and Eva Zangerle: Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection. In 11th International Conference of the CLEF Association (CLEF 2020). Springer, 2020

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Janek Bevendorff, Bilal Ghanem, Anastasia Giachanou, Mike Kestemont, Enrique Manjavacas, Martin Potthast, Francisco Rangel, Paolo Rosso, Günther Specht, Efstathios Stamatatos, Benno Stein, Matti Wiegmann and Eva Zangerle: Shared Tasks on Authorship Analysis at PAN 2020. In Advances in Information Retrieval (ECIR 2020), pages 508-516. Springer International Publishing, 2020

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Eva Zangerle, Martin Pichl and Markus Schedl: User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. In Transactions of the International Society for Music Information Retrieval, vol. 3, no. 1. Ubiquity Press, 2020

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Meijun Liu, Eva Zangerle, Xiao Hu, Alessandro Melchiorre and Markus Schedl: Pandemics, Music, and Collective Sentiment: Evidence from the Outbreak of COVID-19. In Proceedings of the 21st International Society for Music Information Retrieval Conference 2020 (ISMIR 2020), pages 157-165. 2020

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Michael Vötter, Maximilian Mayerl, Günther Specht and Eva Zangerle: Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups. In Working Notes Proceedings of the MediaEval 2020 Workshop. ceur-ws.org, 2020

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Eva Zangerle, Maximilian Mayerl, Günther Specht, Martin Potthast and Benno Stein: Overview of the style change detection task at PAN 2020. In CLEF 2020 Working Notes, CEUR Workshop Proceedings 2696, Paper 256. 9 S. 2020

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Janek Bevendorff, Bilal Ghanem, Anastasia Giachanou, Mike Kestemont, Enrique Manjavacas, Ilia Markov, Maximilian Mayerl, Martin Potthast, Francisco Rangel, Paolo Rosso and others: Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection. In International Conference of the Cross-Language Evaluation Forum for European Languages (CLEF 2020), pages 372-383. 2020.

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Clemens Hörtenhuemer and Eva Zangerle: A Multi-Aspect Classification Ensemble Approach for Profiling Fake News Spreaders on Twitter. In Proceedings of the International Conference and Labs of the Evaluation Forum (CLEF), Thessaloniki, Greece, pages 22-25. 2020.

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Alessandro B. Melchiorre, Eva Zangerle and Markus Schedl: Personality Bias of Music Recommendation Algorithms. In 14th ACM Conference on Recommender Systems (RecSys 2020), pages 533–538. ACM, 2020.

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Maximilian Mayerl, Michael Vötter, Manfred Moosleitner and Eva Zangerle: Comparing Lyrics Features for Genre Recognition. In Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), pages 73-77. 2020.

2019

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Asir Saeed, Suzana Ilic and Eva Zangerle: Creating GANs for generating poems, lyrics and metaphors. In NeurIPS Machine Learning for Creativity and Design Workshop, 2019

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Eva Zangerle, Ramona Huber, Michael Vötter and Yi-Hsuan Yang: Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), pages 319-326. 2019

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Christine Bauer and Eva Zangerle: Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings. In Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with 13th ACM Conference on Recommender Systems (ACM RecSys 2019). ceur-ws.org, 2019