Assoc. Prof. Dr. Eva Zangerle

@article{peintner:tors:2025,
 address = {New York, NY, USA},
 articleno = {46},
 author = {Peintner, Andreas and Mohammadi, Amir Reza and Zangerle, Eva},
 doi = {10.1145/3701764},
 issue_date = {December 2025},
 journal = {ACM Transactions on Recommender Systems},
 keywords = {Recommender systems, session-based recommendation, graph neural network, supervised contrastive learning},
 month = {April},
 number = {4},
 numpages = {24},
 publisher = {Association for Computing Machinery},
 title = {Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search},
 url = {https://doi.org/10.1145/3701764},
 volume = {3},
 year = {2025}
}

@article{jacobsen:musiscaescientiae:2024,
	title        = {Assessing aesthetic music-evoked emotions in a minute or less: A comparison of the GEMS-45 and the GEMS-9},
	author       = {Peer-Ole Jacobsen and Hannah Strauss and Julia Vigl and Eva Zangerle and Marcel Zentner},
journal = {Musicae Scientiae},
volume = {29},
number = {1},
pages = {184-192},
year = {2025},
doi = {10.1177/10298649241256252},
}

@article{peintner:tismir:2025,
 abstract = {Music emotion recognition (MER) seeks to understand the complex emotional landscapes elicited by music, acknowledging music\’s profound social and psychological roles beyond traditional tasks such as genre classification or content similarity. MER relies heavily on high‑quality emotional annotations, which serve as the foundation for training models to recognize emotions. However, collecting these annotations is both complex and costly, leading to limited availability of large‑scale datasets for MER. Recent efforts in MER for automatically extracting emotion have focused on learning track representations in a supervised manner. However, these approaches mainly use simplified emotion models due to limited datasets or a lack of necessity for sophisticated emotion models and ignore hidden inter‑track relations, which are beneficial in a semi‑supervised learning setting. This paper proposes a novel approach to MER by constructing a multi‑relational graph that encapsulates different facets of music. We leverage graph neural networks to model intricate inter‑track relationships and capture structurally induced representations from user data, such as listening histories, genres, and tags. Our model, the semi‑supervised multi‑relational graph neural network for emotion recognition (SRGNN‑Emo), innovates by combining graph‑based modeling with semi‑supervised learning, using rich user data to extract nuanced emotional profiles from music tracks. Through extensive experimentation, SRGNN‑Emo demonstrates significant improvements in R2 and root mean squared error metrics for predicting the intensity of nine continuous emotions (Geneva Emotional Music Scale), demonstrating its superior capability in capturing and predicting complex emotional expressions in music.},
 author = {Peintner, Andreas and Moscati, Marta and Kinoshita, Yu and Vogl, Richard and Knees, Peter and Schedl, Markus and Strauss, Hannah and Zentner, Marcel and Zangerle, Eva},
 doi = {10.5334/tismir.235},
 journal = {Transactions of the International Society for Music Information Retrieval},
 keyword = {en_US},
 month = {Jun},
 title = {Nuanced Music Emotion Recognition via a Semi-Supervised Multi-Relational Graph Neural Network},
 year = {2025}
}

@inproceedings{bevendorff_ecir_2025,
 address = {Cham},
 author = {Bevendorff, Janek
and Dementieva, Daryna
and Fröbe, Maik
and Gipp, Bela
and Greiner-Petter, Andre
and Karlgren, Jussi
and Mayerl, Maximilian
and Nakov, Preslav
and Panchenko, Alexander
and Potthast, Martin
and Shelmanov, Artem
and Stamatatos, Efstathios
and Stein, Benno
and Wang, Yuxia
and Wiegmann, Matti
and Zangerle, Eva},
 booktitle = {Advances in Information Retrieval},
 editor = {Hauff, Claudia
and Macdonald, Craig
and Jannach, Dietmar
and Kazai, Gabriella
and Nardini, Franco Maria
and Pinelli, Fabio
and Silvestri, Fabrizio
and Tonellotto, Nicola},
 isbn = {978-3-031-88720-8},
 pages = {434--441},
 publisher = {Springer Nature Switzerland},
 title = {Overview of PAN 2025: Generative AI Detection, Multilingual Text Detoxification, Multi-author Writing Style Analysis, and Generative Plagiarism Detection},
 year = {2025},
 doi = {10.1007/978-3-031-88720-8_64},
 url = {https://doi.org/10.1007/978-3-031-88720-8_64}
}

@inproceedings{peintner2025hyperhawkes,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Michael M{\"{u}}ller and
                  Eva Zangerle},
  title        = {Hypergraph-based Temporal Modelling of Repeated Intent for Sequential
                  Recommendation},
  booktitle    = {Proceedings of the {ACM} on Web Conference 2025, {WWW} 2025, Sydney,
                  NSW, Australia, 28 April 2025- 2 May 2025},
  pages        = {3809--3818},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://doi.org/10.1145/3696410.3714896},
  doi          = {10.1145/3696410.3714896}
}

@Article{bauer:dagstuhl:2024,
  author =	{Bauer, Christine and Said, Alan and Zangerle, Eva},
  title =	{{Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)}},
  pages =	{58--172},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{5},
  editor =	{Bauer, Christine and Said, Alan and Zangerle, Eva},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.58},
  URN =		{urn:nbn:de:0030-drops-222655},
  doi =		{10.4230/DagRep.14.5.58},
  annote =	{Keywords: Recommender Systems, Evaluation, Information Retrieval, User Interaction, Intelligent Systems}
}

@inproceedings{10.1145/3640457.3687101,
author = {Said, Alan and Bauer, Christine and Zangerle, Eva},
title = {Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES)},
year = {2024},
isbn = {9798400705052},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3640457.3687101},
doi = {10.1145/3640457.3687101},
abstract = {With the RecSys conference now turning 18 years old, the recommender systems (RS) discipline ventures into adulthood. This workshop serves as a platform for introspection, examining the evolution of RS from its origins in CHI to its current state heavily influenced by and focusing on machine learning. The INTROSPECTIVES workshop aims to foster discussions on the past, present, and future of the RS discipline, inviting the community to reflect on key questions such as the maturation of RS, shifts in research focus, and the impact and success of RS in practice. Topics include the changing landscape of RS problems, the evolving role of RS in addressing choice overload to the current motivations driving RS adoption.},
booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},
pages = {1237–1238},
numpages = {2},
keywords = {information retrieval, introspection, personalized systems, recommender systems, reflection},
location = {Bari, Italy},
series = {RecSys '24}
}

@InProceedings{10.1007/978-3-031-56072-9_1,
author="Bevendorff, Janek
and Casals, Xavier Bonet
and Chulvi, Berta
and Dementieva, Daryna
and Elnagar, Ashaf
and Freitag, Dayne
and Fr{\"o}be, Maik
and Koren{\v{c}}i{\'{c}}, Damir
and Mayerl, Maximilian
and Mukherjee, Animesh
and Panchenko, Alexander
and Potthast, Martin
and Rangel, Francisco
and Rosso, Paolo
and Smirnova, Alisa
and Stamatatos, Efstathios
and Stein, Benno
and Taul{\'e}, Mariona
and Ustalov, Dmitry
and Wiegmann, Matti
and Zangerle, Eva",
editor="Goharian, Nazli
and Tonellotto, Nicola
and He, Yulan
and Lipani, Aldo
and McDonald, Graham
and Macdonald, Craig
and Ounis, Iadh",
title="Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification",
booktitle="Advances in Information Retrieval",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="3--10",
abstract="The paper gives a brief overview of the four shared tasks organized at the PAN 2024 lab on digital text forensics and stylometry to be hosted at CLEF 2024. The goal of the PAN lab is to advance the state-of-the-art in text forensics and stylometry through an objective evaluation of new and established methods on new benchmark datasets. Our four tasks are: (1) multi-author writing style analysis, which we continue from 2023 in a more difficult version, (2) multilingual text detoxification, a new task that aims to translate and re-formulate text in a non-toxic way, (3) oppositional thinking analysis, a new task that aims to discriminate critical thinking from conspiracy narratives and identify their core actors, and (4) generative AI authorship verification, which formulates the detection of AI-generated text as an authorship problem, one of PAN's core tasks. As with the previous editions, PAN invites software submissions as easy-to-reproduce docker containers; more than 400 pieces of software have been submitted from PAN'12 through PAN'23 combined, with all recent evaluations running on the TIRA experimentation platform [8].",
isbn="978-3-031-56072-9"
}

@inproceedings{zangerle:pan:2024,
  author       = {Eva Zangerle and
                  Maximilian Mayerl and
                  Martin Potthast and
                  Benno Stein},
  title        = {Overview of the Multi-Author Writing Style Analysis Task at {PAN}
                  2024},
  booktitle    = {Working Notes of the Conference and Labs of the Evaluation Forum {(CLEF}
                  2024), Grenoble, France, 9-12 September, 2024},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3740},
  pages        = {2424--2431},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3740},
}

@inproceedings{10.1145/3627043.3659540,
author = {Moscati, Marta and Strau\ss{}, Hannah and Jacobsen, Peer-Ole and Peintner, Andreas and Zangerle, Eva and Zentner, Marcel and Schedl, Markus},
title = {Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags},
year = {2024},
isbn = {9798400704338},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627043.3659540},
doi = {10.1145/3627043.3659540},
abstract = {Emotions constitute an important aspect when listening to music. While manual annotations from user studies grounded in psychological research on music and emotions provide a well-defined and fine-grained description of the emotions evoked when listening to a music track, user-generated tags provide an alternative view stemming from large-scale data. In this work, we examine the relationship between these two emotional characterizations of music and analyze their impact on the performance of emotion-based music recommender systems individually and jointly. Our analysis shows that (i) the agreement between the two characterizations, as measured with Cohen’s κ coefficient and Kendall rank correlation, is often low, (ii) Leveraging the emotion profile based on the intensity of evoked emotions from high-quality annotations leads to performances that are stable across different recommendation algorithms; (iii) Simultaneously leveraging the emotion profiles based on high-quality and large-scale annotations allows to provide recommendations that are less exposed to the low accuracy that algorithms might reach when leveraging one type of data, only.},
booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {159–164},
numpages = {6},
keywords = {Annotation Study, Emotion-based Recommender Systems, Emotions, Music, Music Recommender Systems},
location = {Cagliari, Italy},
series = {UMAP '24}
}

@article{strauss:2024:behaviorresearchmethods,
	title        = {The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts},
	author       = {Strauss, Hannah and Vigl, Julia and Jacobsen, Peer-Ole and Bayer, Martin and Talamini, Francesca and Vigl, Wolfgang and Zangerle, Eva and Zentner, Marcel},
	year         = 2024,
	month        = {jun},
	journal      = {Behavior Research Methods},
	publisher    = {Springer},
	pages        = {3560--3577},
        issue_date   = {June 2024},
	volume	     = 56,
        number       = 4,
	doi          = {https://doi.org/10.3758/s13428-024-02336-0}
}

@proceedings{soto:iuiworkshops:2024,
  editor       = {Axel Soto and
                  Eva Zangerle},
  title        = {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},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3660},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3660},
  urn          = {urn:nbn:de:0074-3660-6},
  timestamp    = {Fri, 12 Apr 2024 20:58:03 +0200},
  biburl       = {https://dblp.org/rec/conf/iui/2024w.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{parada:srep:2024,
  title={Song lyrics have become simpler and more repetitive over the last five decades},
  author={Parada-Cabaleiro, Emilia and Mayerl, Maximilian and Brandl, Stefan and Skowron, Marcin and Schedl, Markus and Lex, Elisabeth and Zangerle, Eva},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={5531},
  year={2024},
  publisher={Nature Publishing Group UK London},
  doi={10.1038/s41598-024-55742-x}
}

@inproceedings{DBLP:conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24,
  author       = {Janek Bevendorff and
                  Xavier Bonet Casals and
                  Berta Chulvi and
                  Daryna Dementieva and
                  Ashaf Elnagar and
                  Dayne Freitag and
                  Maik Fr{\"{o}}be and
                  Damir Korencic and
                  Maximilian Mayerl and
                  Animesh Mukherjee and
                  Alexander Panchenko and
                  Martin Potthast and
                  Francisco Rangel and
                  Paolo Rosso and
                  Alisa Smirnova and
                  Efstathios Stamatatos and
                  Benno Stein and
                  Mariona Taul{\'{e}} and
                  Dmitry Ustalov and
                  Matti Wiegmann and
                  Eva Zangerle},
  editor       = {Nazli Goharian and
                  Nicola Tonellotto and
                  Yulan He and
                  Aldo Lipani and
                  Graham McDonald and
                  Craig Macdonald and
                  Iadh Ounis},
  title        = {Overview of {PAN} 2024: Multi-author Writing Style Analysis, Multilingual
                  Text Detoxification, Oppositional Thinking Analysis, and Generative
                  {AI} Authorship Verification - Extended Abstract},
  booktitle    = {Advances in Information Retrieval - 46th European Conference on Information
                  Retrieval, {ECIR} 2024, Glasgow, UK, March 24-28, 2024, Proceedings,
                  Part {VI}},
  series       = {Lecture Notes in Computer Science},
  volume       = {14613},
  pages        = {3--10},
  publisher    = {Springer},
  year         = {2024},
  doi          = {10.1007/978-3-031-56072-9\_1},
}

@article{10.1145/3648398,
author = {Bauer, Christine and Said, Alan and Zangerle, Eva},
title = {Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation},
year = {2024},
issue_date = {March 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {2},
number = {1},
url = {https://doi.org/10.1145/3648398},
doi = {10.1145/3648398},
abstract = {Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to elaborate implementations of systems integrated into everyday product settings; it may target a wide spectrum of different factors being evaluated. In this special issue, we explore recommender systems evaluation—theory and practice—while considering a diverse set of perspectives. These include recommender systems purposes, stakeholders, methodological approaches, and consequences. The collection of articles in this special issue offers insightful analyses of current recommender system evaluation practices, acknowledging their limitations, and setting out future research directions. As recommender systems evolve, the need for adequate evaluation methods and approaches increases. This special issue sheds light on areas undergoing development or requiring added attention from the research and practitioner communities in recommender systems. The compilation serves as a call to the recommender systems research community, motivating continued research and exploration of evaluation metrics, methods, and strategies.},
journal = {ACM Trans. Recomm. Syst.},
month = {mar},
articleno = {1},
numpages = {5},
keywords = {Evaluation, recommender systems, reproducibility, datasets, metrics}
}

@article{10.1145/3629170,
author = {Bauer, Christine and Zangerle, Eva and Said, Alan},
title = {Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives},
year = {2024},
issue_date = {March 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {2},
number = {1},
url = {https://doi.org/10.1145/3629170},
doi = {10.1145/3629170},
abstract = {Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this article, we present an overview of research specifically focused on the evaluation of recommender systems. We perform a systematic literature review, in which we analyze 57 papers spanning six years (2017–2022). Focusing on the processes surrounding evaluation, we dial in on the methods applied, the datasets utilized, and the metrics used. Our study shows that the predominant experiment type in research on the evaluation of recommender systems is offline experimentation and that online evaluations are primarily used in combination with other experimentation methods, e.g., an offline experiment. Furthermore, we find that only a few datasets (MovieLens, Amazon review dataset) are widely used, while many datasets are used in only a few papers each. We observe a similar scenario when analyzing the employed performance metrics—a few metrics are widely used (precision, normalized Discounted Cumulative Gain, and Recall), while many others are used in only a few papers. Overall, our review indicates that beyond-accuracy qualities are rarely assessed. Our analysis shows that the research community working on evaluation has focused on the development of evaluation in a rather narrow scope, with the majority of experiments focusing on a few metrics, datasets, and methods.},
journal = {ACM Trans. Recomm. Syst.},
month = {mar},
articleno = {11},
numpages = {31},
keywords = {Evaluation, survey, systematic literature review, recommender systems}
}

@article{10.1145/3642979.3643000,
author = {Said, Alan and Zangerle, Eva and Bauer, Christine},
title = {Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023},
year = {2024},
issue_date = {December 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {57},
number = {2},
issn = {0163-5840},
url = {https://doi.org/10.1145/3642979.3643000},
doi = {10.1145/3642979.3643000},
journal = {SIGIR Forum},
month = {jan},
articleno = {18},
numpages = {4}
}

@inproceedings{DBLP:conf/clef/ZangerleMP023,
  author       = {Eva Zangerle and
                  Maximilian Mayerl and
                  Martin Potthast and
                  Benno Stein},
  editor       = {Mohammad Aliannejadi and
                  Guglielmo Faggioli and
                  Nicola Ferro and
                  Michalis Vlachos},
  title        = {Overview of the Multi-Author Writing Style Analysis Task at {PAN}
                  2023},
  booktitle    = {Working Notes of the Conference and Labs of the Evaluation Forum {(CLEF}
                  2023), Thessaloniki, Greece, September 18th to 21st, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3497},
  pages        = {2513--2522},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3497/paper-201.pdf},
  timestamp    = {Wed, 25 Oct 2023 17:24:28 +0200},
  biburl       = {https://dblp.org/rec/conf/clef/ZangerleMP023.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{bevendorff:clef:2023,
author="Bevendorff, Janek
and Borrego-Obrador, Ian
and Chinea-R{\'i}os, Mara
and Franco-Salvador, Marc
and Fr{\"o}be, Maik
and Heini, Annina
and Kredens, Krzysztof
and Mayerl, Maximilian
and P{\k{e}}zik, Piotr
and Potthast, Martin
and Rangel, Francisco
and Rosso, Paolo
and Stamatatos, Efstathios
and Stein, Benno
and Wiegmann, Matti
and Wolska, Magdalena
and Zangerle, Eva",
title="Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection",
booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="459--481",
abstract="The paper gives a brief overview of three shared tasks which have been organized at the PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, profiling cryptocurrency influencers with few-shot learning, and trigger detection. Authorship verification and multi-author analysis continue and advance from past editions of PAN and influencer profiling and trigger detection are new tasks with novel research questions and evaluation resources. All four tasks alilgn with the goals of all shared tasks at PAN: to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.",
isbn="978-3-031-42448-9",
doi="10.1007/978-3-031-42448-9_29"
}

@inproceedings{DBLP:conf/recsys/PeintnerMZ23,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Eva Zangerle},
  title        = {{SPARE:} Shortest Path Global Item Relations for Efficient Session-based
                  Recommendation},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {58--69},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608768},
  doi          = {10.1145/3604915.3608768}
}