Eva Zangerle is a postdoctoral researcher 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. Eva Zangerle teaches at the University of Innsbruck, Austria and at the University of Applied Sciences in Salzburg, Austria.
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, pages 508-516. Springer International Publishing, 2020
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
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, 2020 (to appear).
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, 2020 (to appear).
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
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
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
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.
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
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
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
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
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
Bettina Larl and Eva Zangerle: Leiwand Oida: Geolocating Regional Linguistic Variation of German on Twitter. In Proceedings of the 6th Conference on Computer-Mediated Communication (CMC) and Social Media Corpora (CMC-corpora 2018). 2018
Martin Pichl and Eva Zangerle: Latent Feature Combination for Multi-Context Music Recommendation. In 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pages 1-6. 2018
Eva Zangerle and Martin Pichl: Content-based User Models: Modeling the Many Faces of Musical Preference. In Proceedings of the 19th International Society for Music Information Retrieval Conference 2018 (ISMIR 2018), pages 709-716. 2018
Martin Pichl, Bernward Pichl and Eva Zangerle: Carl: Sports Award Recommender. In The SIGIR 2018 Workshop On eCommerce co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan, USA, July 12, 2018., vol. 2319. CEUR-WS.org, 2018
Asmita Poddar, Eva Zangerle and Yi-Hsuan Yang : #nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems. In Proceedings of the 15th Sound & Music Computing Conference. 2018
Ilknur Celik, Ilaria Torre, Frosina Koceva, Christine Bauer, Eva Zangerle and Bart Knijnenburg: Intelligent User-Adapted Interfaces: Design and Multi-Modal Evaluation (IUadaptMe) Workshop Chairs' Welcome and Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP 2018), pages 137-139. ACM, 2018