Eva Zangerle

Dr. Eva Zangerle

Tel: +43 512 507 53236
Fax
+43 512 507 53059
Office
ICT building, 2nd floor, room 3W02
Consultation Hours

Eva Zangerle is an assistant professor at the University of Innsbruck at the research group for Databases and Information Systems (Department of Computer Science). She earned her PhD from the University of Innsbruck in the field of recommender systems for collaborative social media platforms. Her main research interests are within the fields of social media analysis, recommender systems and information retrieval. Over the last years, she has combined these three fields of research and investigated music recommender systems based on data retrieved from social media platforms aiming to exploit new sources of information for recommender systems. She was awarded a Postdoctoral Fellowship for Overseas Researchers from the Japan Society for the Promotion of Science allowing her to make a short-term research stay at the Ritsumeikan University in Kyoto. 

Publications

2021

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E. Zangerle, C. -M. Chen, M. -F. Tsai and Y. -H. Yang: Leveraging Affective Hashtags for Ranking Music Recommendations. In IEEE Transactions on Affective Computing, vol. 12, no. 1, pages 78-91. 2021

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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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 (to be published)

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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

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Walter Daelemans, Mike Kestemont, Enrique Manjavacas, Martin Potthast, Francisco M. Rangel Pardo, Paolo Rosso, Günther Specht, Efstathios Stamatatos, Benno Stein, Michael Tschuggnall, Matti Wiegmann and Eva Zangerle: Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-Domain Authorship Attribution and Style Change Detection. In Experimental IR Meets Multilinguality, Multimodality, and Interaction - 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9-12, 2019, Proceedings, vol. 11696, pages 402-416.

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Eva Zangerle, Michael Tschuggnall, Günther Specht, Martin Potthast and Benno Stein: Overview of the Style Change Detection Task at PAN 2019. In CLEF 2019 Labs and Workshops, Notebook Papers. CEUR-WS.org, 2019

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Manuel Schmidt and Eva Zangerle: Article Quality Classification on Wikipedia: Introducing Document Embeddings and Content Features. In Proceedings of the 15th International Symposium on Open Collaboration, OpenSym 2019, Skövde, Sweden, August 20-22, 2019, pages 13:1-13:8. ACM, 2019

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Maximilian Mayerl, Michael Vötter, Eva Zangerle and Günther Specht: Language Models for Next-Track Music Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 15-19. 2019

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Michael Vötter, Eva Zangerle, Maximilian Mayerl and Günther Specht: Autoencoders for Next-Track-Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 20-25. 2019

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Gerald Hiebel, Klaus Hanke, Claudia Posch, Gerhard Rampl, Elisabeth Gruber, Andrea Mussmann and Eva Zangerle: Zur Identifikation und Verortung von Bergnamen in alpiner Literatur. In 20. Internationale Geodätische Woche Obergurgl 2019, pages 91-100. VDE Verlag, 2019

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Maximilian Mayerl, Michael Vötter, Hsiao-Tzu Hung, Boyu Chen, Yi-Hsuan Yang and Eva Zangerle: Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks. In Working Notes Proceedings of the MediaEval 2019 Workshop. ceur-ws.org, 2019.

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Hsiao-Tzu Hung, Yu-Hua Chen, Maximilian Mayerl, Michael Vötter, Eva Zangerle and Yi-Hsuan Yang: MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach. In Working Notes Proceedings of the MediaEval 2019 Workshop. ceur-ws.org, 2019.