World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7423
World Ranking
8390
National Ranking
231

Tommaso Di Noia 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 Tommaso Di Noia 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: 333 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Tommaso Di Noia 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 Tommaso Di Noia 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Tommaso Di Noia is affiliated with the Polytechnic University of Bari in Italy and has a significant record of scientific contributions in the field of Computer Science. Their research activities encompass various subfields, including Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Cognitive Neuroscience, and Nephrology.

Their work is concentrated on several main topics, such as:

  • Recommender Systems and Techniques
  • Topic Modeling
  • Advanced Graph Neural Networks
  • Adversarial Robustness in Machine Learning
  • Machine Learning in Healthcare
  • Generative Adversarial Networks and Image Synthesis
  • Explainable Artificial Intelligence (XAI)

Recent publications by this researcher include:

  • "A Survey on Adversarial Recommender Systems," 2021, published in ACM Computing Surveys
  • "Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy," 2020, Kidney International
  • "A Review of Modern Fashion Recommender Systems," 2023, ACM Computing Surveys
  • "Recommender systems under European AI regulations," 2022, Communications of the ACM
  • "A flexible framework for evaluating user and item fairness in recommender systems," 2021, User Modeling and User-Adapted Interaction

The researcher frequently publishes in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Expert Systems with Applications
  • ACM Computing Surveys
  • Journal of Nephrology

Collaborations have been established with several co-authors, including:

  • Eugenio Di Sciascio
  • Vito Walter Anelli
  • Yashar Deldjoo
  • Claudio Pomo
  • Daniele Malitesta

Best Publications

  • A System for Principled Matchmaking in an Electronic Marketplace

    Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini;Marina Mongiello

  • Linked open data to support content-based recommender systems

    Tommaso Di Noia;Roberto Mirizzi;Vito Claudio Ostuni;Davide Romito

  • RDF2Vec: RDF graph embeddings and their applications

    Petar Ristoski;Jessica Rosati;Jessica Rosati;Tommaso Di Noia;Renato De Leone

  • Top-N recommendations from implicit feedback leveraging linked open data

    Vito Claudio Ostuni;Tommaso Di Noia;Eugenio Di Sciascio;Roberto Mirizzi

  • A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks

    Yashar Deldjoo;Tommaso Di Noia;Felice Antonio Merra

  • Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace

    Simona Colucci;Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini

  • Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

    Vito Walter Anelli;Alejandro Bellogin;Antonio Ferrara;Daniele Malitesta

  • Sound and Music Recommendation with Knowledge Graphs

    Sergio Oramas;Vito Claudio Ostuni;Tommaso Di Noia;Xavier Serra

  • Semantic matchmaking as non-monotonic reasoning: a description logic approach

    Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini

  • A Formal Approach to Ontology-Based Semantic Match of Skills Descriptions.

    Simona Colucci;Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini

  • SPrank: Semantic Path-Based Ranking for Top- N Recommendations Using Linked Open Data

    Tommaso Di Noia;Vito Claudio Ostuni;Paolo Tomeo;Eugenio Di Sciascio

  • Exploiting the web of data in model-based recommender systems

    Tommaso Di Noia;Roberto Mirizzi;Vito Claudio Ostuni;Davide Romito

  • Abductive matchmaking using description logics

    Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini;Marina Mongiello

  • An analysis of users' propensity toward diversity in recommendations

    Tommaso Di Noia;Vito Claudio Ostuni;Jessica Rosati;Paolo Tomeo

  • Recommender systems under European AI regulations

    Unknown

  • Concept Abduction and Contraction in Description Logics.

    Simona Colucci;Tommaso Di Noia;Eugenio Di Sciascio;Francesco M. Donini

  • Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization

    Ignacio Fernández-Tobías;Iván Cantador;Paolo Tomeo;Vito Walter Anelli

  • An end stage kidney disease predictor based on an artificial neural networks ensemble

    Tommaso Di Noia;Vito Claudio Ostuni;Francesco Pesce;Giulio Binetti

  • Recommender Systems and Linked Open Data

    Tommaso Di Noia;Vito Claudio Ostuni

  • Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

    Unknown

  • Ranking the linked data: the case of DBpedia

    Roberto Mirizzi;Azzurra Ragone;Tommaso Di Noia;Eugenio Di Sciascio

  • A Nonmonotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces

    Simona Colucci;Tommaso Di Noia;Agnese Pinto;Michele Ruta

  • Top-N Recommendations from Implicit Feedback Leveraging Linked Open Data.

    Vito Claudio Ostuni;Tommaso Di Noia;Roberto Mirizzi;Eugenio Di Sciascio

Frequent Co-Authors

Eugenio Di Sciascio
Eugenio Di Sciascio Polytechnic University of Bari
Francesco M. Donini
Francesco M. Donini Tuscia University
Thomas Lukasiewicz
Thomas Lukasiewicz University of Oxford
Umberto Straccia
Umberto Straccia National Research Council (CNR)
Pasquale Lops
Pasquale Lops University of Bari Aldo Moro
Marco de Gemmis
Marco de Gemmis University of Bari Aldo Moro
Iván Cantador
Iván Cantador Autonomous University of Madrid
Giovanni Semeraro
Giovanni Semeraro University of Bari Aldo Moro
Francesco Paolo Schena
Francesco Paolo Schena University of Bari Aldo Moro
Markus Zanker
Markus Zanker Free University of Bozen-Bolzano

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