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D-Index & Metrics

Computer Science

D-Index
42
Citations
7423
World Ranking
8391
National Ranking
231

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
Iván Cantador
Iván Cantador Autonomous University of Madrid
Marco de Gemmis
Marco de Gemmis University of Bari Aldo Moro
Giovanni Semeraro
Giovanni Semeraro University of Bari Aldo Moro
Markus Zanker
Markus Zanker Free University of Bozen-Bolzano
David Naso
David Naso Polytechnic University of Bari

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