World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
78
Citations
79531
World Ranking
1159
National Ranking
614

John Riedl 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 John Riedl 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: 249 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: 559 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: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 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: 210 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

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

John Riedl 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 John Riedl sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 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: 78 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2010 - ACM Software System Award For the GroupLens Collaborative Filtering Recommender Systems, which showed how to automate the process by which a distributed set of users could receive personalized recommendations by sharing ratings, leading to both commercial products and extensive research.
  • 2009 - ACM Fellow For contributions to recommender systems and to social and collaborative computing.
  • 2007 - ACM Distinguished Member

Overview

John Riedl was affiliated with the University of Minnesota in the United States. Their research was positioned within the field of Computer Science, focusing specifically on Artificial Intelligence.

Their work frequently addressed topics related to advanced text analysis techniques.

John Riedl authored papers published in venues such as:

  • Proceedings of the International AAAI Conference on Web and Social Media

One of their recent papers included:

  • War Versus Inspirational in Forrest Gump: Cultural Effects in Tagging Communities (2021), published in Proceedings of the International AAAI Conference on Web and Social Media

The scientist collaborated with several co-authors, including:

  • Zhenhua Dong
  • Chuan Shi
  • Shilad Sen
  • Loren Terveen

John Riedl received recognition in the form of awards. These included:

  • ACM Software System Award in 2010, awarded for the GroupLens Collaborative Filtering Recommender Systems, which demonstrated automating personalized recommendations by sharing user ratings, influencing both commercial products and research.
  • ACM Fellow in 2009, recognized for contributions to recommender systems and social and collaborative computing.
  • ACM Distinguished Member in 2007.

Best Publications

  • Item-based collaborative filtering recommendation algorithms

    Badrul Sarwar;George Karypis;Joseph Konstan;John Riedl

  • GroupLens: An Open Architecture for Collaborative Filtering of Netnews

    Paul Resnick;Neophytos Iacovou;Mitesh Suchak;Peter Bergstrom

  • Evaluating collaborative filtering recommender systems

    Jonathan L. Herlocker;Joseph A. Konstan;Loren G. Terveen;John T. Riedl

  • An algorithmic framework for performing collaborative filtering

    Jonathan L. Herlocker;Joseph A. Konstan;Al Borchers;John Riedl

  • GroupLens: applying collaborative filtering to Usenet news

    Joseph A. Konstan;Bradley N. Miller;David Maltz;Jonathan L. Herlocker

  • Analysis of recommendation algorithms for e-commerce

    Badrul Sarwar;George Karypis;Joseph Konstan;John Riedl

  • E-Commerce Recommendation Applications

    J. Ben Schafer;Joseph A. Konstan;John Riedl

  • Recommender systems in e-commerce

    J. Ben Schafer;Joseph Konstan;John Riedl

  • An algorithmic framework for performing collaborative filtering

    Unknown

  • Explaining collaborative filtering recommendations

    Jonathan L. Herlocker;Joseph A. Konstan;John Riedl

  • Application of Dimensionality Reduction in Recommender System - A Case Study

    Badrul Sarwar;George Karypis;Joseph Konstan;John T. Riedl

  • Collaborative Filtering Recommender Systems

    Michael D. Ekstrand;John T. Riedl;Joseph A. Konstan

  • Being accurate is not enough: how accuracy metrics have hurt recommender systems

    Sean M. McNee;John Riedl;Joseph A. Konstan

  • Combining collaborative filtering with personal agents for better recommendations

    Nathaniel Good;J. Ben Schafer;Joseph A. Konstan;Al Borchers

  • Recommender systems: from algorithms to user experience

    Joseph A. Konstan;John Riedl

  • An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms

    Jon Herlocker;Joseph A. Konstan;John Riedl

  • Building Successful Online Communities: Evidence-Based Social Design

    Robert E. Kraut;Paul Resnick;Sara Kiesler;Yuqing Ren

  • Shilling recommender systems for fun and profit

    Shyong K. Lam;John Riedl

  • Getting to know you: learning new user preferences in recommender systems

    Al Mamunur Rashid;Istvan Albert;Dan Cosley;Shyong K. Lam

  • MovieLens unplugged: experiences with an occasionally connected recommender system

    Bradley N. Miller;Istvan Albert;Shyong K. Lam;Joseph A. Konstan

  • Is seeing believing?: how recommender system interfaces affect users' opinions

    Dan Cosley;Shyong K. Lam;Istvan Albert;Joseph A. Konstan

Frequent Co-Authors

Joseph A. Konstan
Joseph A. Konstan University of Minnesota
Bharat Bhargava
Bharat Bhargava Purdue University West Lafayette
Loren Terveen
Loren Terveen University of Minnesota
Ed H. Chi
Ed H. Chi Google (United States)
George Karypis
George Karypis University of Minnesota
Dan Cosley
Dan Cosley National Science Foundation
Mark Claypool
Mark Claypool Worcester Polytechnic Institute
Robert E. Kraut
Robert E. Kraut Carnegie Mellon University
Gloria Mark
Gloria Mark University of California, Irvine
Sara Kiesler
Sara Kiesler Carnegie Mellon University

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