World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
6848
World Ranking
8863
National Ranking
130

Iván Cantador 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 Iván Cantador 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: 134 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Iván Cantador 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 Iván Cantador 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Iván Cantador is affiliated with the Autonomous University of Madrid in Spain and has produced research primarily within the fields of Computer Science and Social Sciences. Their work encompasses a range of topics with a focus on Artificial Intelligence, Information Systems, and interdisciplinary social sciences including Sociology and Political Science.

Their research has extensively covered the following main topics:

  • Recommender Systems and Techniques
  • Topic Modeling
  • Advanced Graph Neural Networks
  • Smart Cities and Technologies
  • E-Government and Public Services
  • Human Mobility and Location-Based Analysis
  • Privacy, Security, and Data Protection

Iván Cantador's publication record includes papers in various scholarly venues with multiple articles featured in arXiv (Cornell University) as well as contributions to Information Systems, Government Information Quarterly, and Information Processing & Management. Frequent publication venues include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Information Systems
  • Government Information Quarterly
  • Information Processing & Management

Notable recent papers authored or co-authored by Iván Cantador include the following:

  • "Recommender systems for smart cities," 2020, Information Systems
  • "Trends and challenges of e-government chatbots: Advances in exploring open government data and citizen participation content," 2023, Government Information Quarterly
  • "Exploiting Open Data to analyze discussion and controversy in online citizen participation," 2020, Information Processing & Management
  • "Recommender systems in model-driven engineering," 2021, Software & Systems Modeling
  • "Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation," 2021, arXiv (Cornell University)

Collaborations are a significant aspect of their scholarly work, with frequent coauthors including:

  • María E. Cortés-Cediel
  • Andrés Carvallo
  • Andrés Segura-Tinoco
  • Alejandro Bellogín
  • Olga Gil

The research scope of Iván Cantador spans practical and theoretical implications in the development of recommender systems and the use of open data to support citizen participation and governance. Their contributions also address the challenges of misinformation amplification via algorithmic recommendations and the integration of advanced graph neural networks in data modeling contexts.

Best Publications

  • Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

    Pedro G. Campos;Fernando Díez;Iván Cantador

  • Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

    Peter Brusilovsky;Iván Cantador;Yehuda Koren;Tsvi Kuflik

  • Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

    Unknown

  • Semantically enhanced Information Retrieval: An ontology-based approach

    Miriam Fernández;Iván Cantador;Vanesa López;David Vallet

  • Precision-oriented evaluation of recommender systems: an algorithmic comparison

    Alejandro Bellogin;Pablo Castells;Ivan Cantador

  • Content-based recommendation in social tagging systems

    Iván Cantador;Alejandro Bellogín;David Vallet

  • Personalizing web search with folksonomy-based user and document profiles

    David Vallet;Iván Cantador;Joemon M. Jose

  • Cross-Domain Recommender Systems.

    Iván Cantador;Ignacio Fernández-Tobías;Shlomo Berkovsky;Paolo Cremonesi

  • Categorising social tags to improve folksonomy-based recommendations

    Iván Cantador;Ioannis Konstas;Joemon M. Jose

  • Statistical biases in Information Retrieval metrics for recommender systems

    Alejandro Bellogín;Pablo Castells;Iván Cantador

  • Alleviating the new user problem in collaborative filtering by exploiting personality information

    Ignacio Fernández-Tobías;Matthias Braunhofer;Mehdi Elahi;Francesco Ricci

  • Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis

    Martin Szomszor;Harith Alani;Ivan Cantador;Kieron O'Hara

  • Relating personality types with user preferences in multiple entertainment domains

    Iván Cantador;Ignacio Fernández-Tobías;Alejandro Bellogín

  • A multilayer ontology-based hybrid recommendation model

    Iván Cantador;Alejandro Bellogín;Pablo Castells

  • Ontology-Based Personalised and Context-Aware Recommendations of News Items

    Iván Cantador;Alejandro Bellogín;Pablo Castells

  • A generic semantic-based framework for cross-domain recommendation

    Ignacio Fernández-Tobías;Iván Cantador;Marius Kaminskas;Francesco Ricci

  • Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) : 27th October 2011, Chicago, IL, USA

    Iván Cantador;Peter Brusilovsky;Tsvi Kuflik

  • Recommender systems for smart cities

    Lara Quijano-Sánchez;Lara Quijano-Sánchez;Iván Cantador;María E. Cortés-Cediel;Olga Gil

  • Correlating user profiles from multiple folksonomies

    Martin N. Szomszor;Iván Cantador;Harith Alani

  • An empirical comparison of social, collaborative filtering, and hybrid recommenders

    Alejandro Bellogín;Iván Cantador;Fernando Díez;Pablo Castells

  • Multilayered semantic social network modeling by ontology-based user profiles clustering: application to collaborative filtering

    Iván Cantador;Pablo Castells

  • Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations

    Iván Cantador;Martin Szomszor;Harith Alani;Miriam Fernández

Frequent Co-Authors

Pablo Castells
Pablo Castells Autonomous University of Madrid
Joemon M. Jose
Joemon M. Jose University of Glasgow
Tommaso Di Noia
Tommaso Di Noia Polytechnic University of Bari
Francesco Ricci
Francesco Ricci Free University of Bozen-Bolzano
Harith Alani
Harith Alani The Open University
Peter Brusilovsky
Peter Brusilovsky University of Pittsburgh
Esther Guerra
Esther Guerra Autonomous University of Madrid
Tsvi Kuflik
Tsvi Kuflik University of Haifa
Juan de Lara
Juan de Lara Autonomous University of Madrid

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