2026 Markus Schedl: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	54	4649	4519	31	30	300	9200

Overview

Markus Schedl is a researcher affiliated with Johannes Kepler University of Linz in Austria. Their work spans a range of topics primarily within computer science, with a focus on artificial intelligence, signal processing, and information systems. Schedl has contributed extensively to the fields of music and audio processing as well as recommender systems and techniques.

Their research has been published in multiple venues, including frequent appearances in arXiv (Cornell University) and Zenodo (CERN European Organization for Nuclear Research). Other notable publication venues include the Transactions of the International Society for Music Information Retrieval, the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, and the International Journal of Multimedia Information Retrieval.

Schedl's recent academic papers include the following:

Content-driven music recommendation: Evolution, state of the art, and challenges, 2024, Computer Science Review
Explainability in music recommender systems, 2022, AI Magazine
Recommender systems under European AI regulations, 2022, Communications of the ACM
Investigating gender fairness of recommendation algorithms in the music domain, 2021, Information Processing & Management
Recommender Systems Leveraging Multimedia Content, 2020, ACM Computing Surveys

Collaboration is a significant aspect of Schedl's research, with frequent co-authors including Elisabeth Lex, Dominik Kowald, Emilia Parada-Cabaleiro, Eva Zangerle, and Marta Moscati.

The researcher's main fields of study characterized by number of publications include:

Computer Science

The subfields of study include:

Artificial Intelligence
Signal Processing
Information Systems
Computer Vision and Pattern Recognition
Cognitive Neuroscience

Main research topics covered by Schedl's work are:

Music and Audio Processing
Recommender Systems and Techniques
Neuroscience and Music Perception
Privacy-Preserving Technologies in Data
Music Technology and Sound Studies
Ethics and Social Impacts of AI
Topic Modeling

Schedl has also contributed to the academic literature through book publication. One of the known works is published in the "information retrieval series" publisher, titled Technical and Regulatory Perspectives on Information Retrieval and Recommender Systems, released in 2024.

Best Publications

Current challenges and visions in music recommender systems research

Markus Schedl;Hamed Zamani;Ching-Wei Chen;Yashar Deldjoo

374
Citations
Music Information Retrieval: Recent Developments and Applications

Markus Schedl;Emilia Gómez;Julián Urbano

356
Citations
Recommender Systems Leveraging Multimedia Content

Yashar Deldjoo;Markus Schedl;Paolo Cremonesi;Gabriella Pasi

229
Citations
The LFM-1b Dataset for Music Retrieval and Recommendation

Markus Schedl

217
Citations
Polyphonic piano note transcription with recurrent neural networks

Sebastian Bock;Markus Schedl

193
Citations
A survey of music similarity and recommendation from music context data

Peter Knees;Markus Schedl

185
Citations
The neglected user in music information retrieval research

Markus Schedl;Arthur Flexer;Julián Urbano

182
Citations
Evaluating the Online Capabilities of Onset Detection Methods.

Sebastian Böck;Florian Krebs;Markus Schedl

168
Citations
Fusing Social Media Cues: Personality Prediction from Twitter and Instagram

Marcin Skowron;Marko Tkalčič;Bruce Ferwerda;Markus Schedl

158
Citations
Music Recommender Systems

Markus Schedl;Peter Knees;Brian McFee;Dmitry Bogdanov

152
Citations
An innovative three-dimensional user interface for exploring music collections enriched

Peter Knees;Markus Schedl;Tim Pohle;Gerhard Widmer

144
Citations
A music search engine built upon audio-based and web-based similarity measures

Peter Knees;Tim Pohle;Markus Schedl;Gerhard Widmer

139
Citations
Investigating gender fairness of recommendation algorithms in the music domain

Alessandro B. Melchiorre;Navid Rekabsaz;Emilia Parada-Cabaleiro;Stefan Brandl

136
Citations
ENHANCED BEAT TRACKING WITH CONTEXT-AWARE NEURAL NETWORKS

Sebastian Böck;Markus Schedl

134
Citations
Location-aware music recommendation using auto-tagging and hybrid matching

Marius Kaminskas;Francesco Ricci;Markus Schedl

126
Citations
Predicting Personality Traits with Instagram Pictures

Bruce Ferwerda;Markus Schedl;Marko Tkalcic

117
Citations
Local and global scaling reduce hubs in space

Dominik Schnitzer;Arthur Flexer;Markus Schedl;Gerhard Widmer

116
Citations
Recsys challenge 2018: automatic music playlist continuation

Ching-Wei Chen;Paul Lamere;Markus Schedl;Hamed Zamani

113
Citations
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

Dominik Kowald;Markus Schedl;Elisabeth Lex

112
Citations
Deep Learning in Music Recommendation Systems

Markus Schedl

109
Citations
ON RHYTHM AND GENERAL MUSIC SIMILARITY

Tim Pohle;Dominik Schnitzer;Markus Schedl;Peter Knees

106
Citations
Using Instagram Picture Features to Predict Users' Personality

Bruce Ferwerda;Markus Schedl;Marko Tkalcic

104
Citations

Frequent Co-Authors

Gerhard Widmer Johannes Kepler University of Linz

Paolo Cremonesi Polytechnic University of Milan

Emilia Gómez Pompeu Fabra University

Xiao Hu University of Hong Kong

Jialie Shen City, University of London

Yu-Gang Jiang Fudan University

Masataka Goto National Institute of Advanced Industrial Science and Technology

Andreas Rauber TU Wien

Xavier Serra Pompeu Fabra University

Gabriella Pasi University of Milano-Bicocca

External Links

Personal website of Markus Schedl Google Scholar page

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