World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
9200
World Ranking
4649
National Ranking
31

Markus Schedl publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Markus Schedl sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 300 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Markus Schedl D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Markus Schedl sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

  • Music Information Retrieval: Recent Developments and Applications

    Markus Schedl;Emilia Gómez;Julián Urbano

  • Recommender Systems Leveraging Multimedia Content

    Yashar Deldjoo;Markus Schedl;Paolo Cremonesi;Gabriella Pasi

  • The LFM-1b Dataset for Music Retrieval and Recommendation

    Markus Schedl

  • Polyphonic piano note transcription with recurrent neural networks

    Sebastian Bock;Markus Schedl

  • A survey of music similarity and recommendation from music context data

    Peter Knees;Markus Schedl

  • The neglected user in music information retrieval research

    Markus Schedl;Arthur Flexer;Julián Urbano

  • Evaluating the Online Capabilities of Onset Detection Methods.

    Sebastian Böck;Florian Krebs;Markus Schedl

  • Fusing Social Media Cues: Personality Prediction from Twitter and Instagram

    Marcin Skowron;Marko Tkalčič;Bruce Ferwerda;Markus Schedl

  • Music Recommender Systems

    Markus Schedl;Peter Knees;Brian McFee;Dmitry Bogdanov

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

    Peter Knees;Markus Schedl;Tim Pohle;Gerhard Widmer

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

    Peter Knees;Tim Pohle;Markus Schedl;Gerhard Widmer

  • Investigating gender fairness of recommendation algorithms in the music domain

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

  • ENHANCED BEAT TRACKING WITH CONTEXT-AWARE NEURAL NETWORKS

    Sebastian Böck;Markus Schedl

  • Location-aware music recommendation using auto-tagging and hybrid matching

    Marius Kaminskas;Francesco Ricci;Markus Schedl

  • Predicting Personality Traits with Instagram Pictures

    Bruce Ferwerda;Markus Schedl;Marko Tkalcic

  • Local and global scaling reduce hubs in space

    Dominik Schnitzer;Arthur Flexer;Markus Schedl;Gerhard Widmer

  • Recsys challenge 2018: automatic music playlist continuation

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

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

    Dominik Kowald;Markus Schedl;Elisabeth Lex

  • Deep Learning in Music Recommendation Systems

    Markus Schedl

  • ON RHYTHM AND GENERAL MUSIC SIMILARITY

    Tim Pohle;Dominik Schnitzer;Markus Schedl;Peter Knees

  • Using Instagram Picture Features to Predict Users' Personality

    Bruce Ferwerda;Markus Schedl;Marko Tkalcic

Frequent Co-Authors

Gerhard Widmer
Gerhard Widmer Johannes Kepler University of Linz
Paolo Cremonesi
Paolo Cremonesi Polytechnic University of Milan
Emilia Gómez
Emilia Gómez Pompeu Fabra University
Xiao Hu
Xiao Hu University of Hong Kong
Jialie Shen
Jialie Shen City, University of London
Yu-Gang Jiang
Yu-Gang Jiang Fudan University
Masataka Goto
Masataka Goto National Institute of Advanced Industrial Science and Technology
Xavier Serra
Xavier Serra Pompeu Fabra University
Gabriella Pasi
Gabriella Pasi University of Milano-Bicocca

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