Eva Zangerle

1.5k total citations
65 papers, 639 citations indexed

About

Eva Zangerle is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Eva Zangerle has authored 65 papers receiving a total of 639 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 32 papers in Information Systems and 31 papers in Signal Processing. Recurrent topics in Eva Zangerle's work include Music and Audio Processing (27 papers), Recommender Systems and Techniques (27 papers) and Topic Modeling (12 papers). Eva Zangerle is often cited by papers focused on Music and Audio Processing (27 papers), Recommender Systems and Techniques (27 papers) and Topic Modeling (12 papers). Eva Zangerle collaborates with scholars based in Austria, Sweden and Netherlands. Eva Zangerle's co-authors include Günther Specht, Martin Pichl, Christine Bauer, W. Gässler, Markus Schedl, Robert Binna, Viktor Leis, Yi‐Hsuan Yang, Alessandro B. Melchiorre and Alan Said and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and ACM Computing Surveys.

In The Last Decade

Eva Zangerle

58 papers receiving 611 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Eva Zangerle Austria	13	340	272	228	166	104	65	639
Yves Raimond United Kingdom	12	207 0.6×	315 1.2×	222 1.0×	188 1.1×	83 0.8×	31	557
Marius Kaminskas Italy	9	388 1.1×	200 0.7×	222 1.0×	229 1.4×	38 0.4×	11	619
Òscar Celma Spain	15	503 1.5×	354 1.3×	512 2.2×	498 3.0×	86 0.8×	43	1.1k
Raluca Paiu Germany	10	339 1.0×	238 0.9×	141 0.6×	207 1.2×	65 0.6×	19	580
Rodger J. McNab New Zealand	12	218 0.6×	285 1.0×	501 2.2×	503 3.0×	56 0.5×	23	894
Palash Nandy United States	4	493 1.4×	289 1.1×	52 0.2×	257 1.5×	129 1.2×	5	765
Anísio Lacerda Brazil	12	351 1.0×	266 1.0×	63 0.3×	125 0.8×	51 0.5×	55	665
Byeong Man Kim South Korea	10	350 1.0×	154 0.6×	106 0.5×	166 1.0×	73 0.7×	40	511
Donald Byrd United States	12	134 0.4×	163 0.6×	272 1.2×	291 1.8×	28 0.3×	27	511
Jeremy Pickens United States	13	230 0.7×	133 0.5×	279 1.2×	299 1.8×	10 0.1×	37	614

Countries citing papers authored by Eva Zangerle

Since Specialization

Citations

This map shows the geographic impact of Eva Zangerle's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eva Zangerle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eva Zangerle more than expected).

Fields of papers citing papers by Eva Zangerle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eva Zangerle. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eva Zangerle. The network helps show where Eva Zangerle may publish in the future.

Co-authorship network of co-authors of Eva Zangerle

This figure shows the co-authorship network connecting the top 25 collaborators of Eva Zangerle. A scholar is included among the top collaborators of Eva Zangerle based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Eva Zangerle. Eva Zangerle is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zangerle, Eva, et al.. (2025). Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems. 515–520.

Talamini, Francesca, et al.. (2024). The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts. Behavior Research Methods. 56(4). 3560–3577. 4 indexed citations

Zangerle, Eva, et al.. (2024). Assessing aesthetic music-evoked emotions in a minute or less: A comparison of the GEMS-45 and the GEMS-9. Musicae Scientiae. 29(1). 184–192. 1 indexed citations

Zangerle, Eva, et al.. (2024). Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags. University Library Linz repository (Johannes Kepler Universitat Linz). 159–164.

Zangerle, Eva, et al.. (2024). Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search. Digital Library of the University of Innsbruck (University of Innsbruck). 3(4). 1–24. 1 indexed citations

Zangerle, Eva, et al.. (2024). Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating Explainers. 1113–1118. 2 indexed citations

Zangerle, Eva, et al.. (2023). SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation. 58–69. 5 indexed citations

Bauer, Christine, Eva Zangerle, & Alan Said. (2023). Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives. 2(1). 1–31. 7 indexed citations

Zangerle, Eva, Christine Bauer, & Alan Said. (2022). Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022. ACM SIGIR Forum. 56(2). 1–4. 1 indexed citations

10.

Zangerle, Eva, et al.. (2020). A Multi-Aspect Classification Ensemble Approach for Profiling Fake News Spreaders on Twitter.. CLEF (Working Notes). 4 indexed citations

11.

Chen, Yuhua, et al.. (2019). MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach.. MediaEval. 3 indexed citations

12.

Zangerle, Eva, Michael Tschuggnall, Günther Specht, Benno Stein, & Martin Potthast. (2019). Overview of the Style Change Detection Task at PAN 2019.. CLEF (Working Notes). 1760–1771. 5 indexed citations

13.

Chen, Boyu, et al.. (2019). Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks.. MediaEval.

14.

Zangerle, Eva, et al.. (2019). Language Models for Next-Track Music Recommendation.. 15–19. 1 indexed citations

15.

Zangerle, Eva & Martin Pichl. (2018). The Many Faces of Users: Modeling Musical Preference. International Symposium/Conference on Music Information Retrieval. 709–716. 3 indexed citations

16.

Tschuggnall, Michael, et al.. (2017). Hierarchical Multilabel Classification and Voting for Genre Classification.. MediaEval. 1 indexed citations

17.

Pichl, Martin, Eva Zangerle, & Günther Specht. (2014). Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. 35–40. 10 indexed citations

18.

Zangerle, Eva, W. Gässler, & Günther Specht. (2013). On the impact of text similarity functions on hashtag recommendations in microblogging environments. Social Network Analysis and Mining. 3(4). 889–898. 39 indexed citations

19.

Binna, Robert, W. Gässler, Eva Zangerle, Dominic Pacher, & Günther Specht. (2011). SpiderStore: A Native Main Memory Approach for Graph Storage. 91–96. 6 indexed citations

20.

Zangerle, Eva & W. Gässler. (2010). Recommendation-Based Evolvement of Dynamic Schemata in Semistructured Information Systems. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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