D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 38 Citations 8,973 327 World Ranking 5062 National Ranking 118

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

Giovanni Semeraro spends much of his time researching Artificial intelligence, Recommender system, Information retrieval, Natural language processing and World Wide Web. His Artificial intelligence research includes elements of Concept learning, Machine learning, Structure and Algorithm. His work on Collaborative filtering as part of general Recommender system study is frequently connected to Serendipity, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

The study incorporates disciplines such as User modeling, Metadata, User profile, Exploit and Profiling in addition to Information retrieval. His User profile research is multidisciplinary, incorporating perspectives in Semantics and User-generated content. His research in World Wide Web focuses on subjects like Context, which are connected to Personalization.

His most cited work include:

  • Content-based Recommender Systems: State of the Art and Trends (1097 citations)
  • A comparative analysis of methods for pruning decision trees (411 citations)
  • A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation (130 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Recommender system, Artificial intelligence, Information retrieval, World Wide Web and Natural language processing. Particularly relevant to Collaborative filtering is his body of work in Recommender system. His research integrates issues of Exploit, Machine learning and Data mining in his study of Artificial intelligence.

His work deals with themes such as Semantics and Context, which intersect with Information retrieval. His Personalization, E-commerce and User-generated content study in the realm of World Wide Web connects with subjects such as Digital library. His biological study spans a wide range of topics, including Word-sense disambiguation, Word, SemEval and Random indexing.

He most often published in these fields:

  • Recommender system (34.20%)
  • Artificial intelligence (32.54%)
  • Information retrieval (30.88%)

What were the highlights of his more recent work (between 2015-2021)?

  • Recommender system (34.20%)
  • Artificial intelligence (32.54%)
  • Information retrieval (30.88%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Recommender system, Artificial intelligence, Information retrieval, Natural language processing and World Wide Web. The Recommender system study combines topics in areas such as Linked data, User profile, Natural language and Human–computer interaction. His study connects Machine learning and Artificial intelligence.

His study focuses on the intersection of Information retrieval and fields such as Representation with connections in the field of Service. His research investigates the connection between Natural language processing and topics such as Distributional semantics that intersect with problems in Context. Social media is the focus of his World Wide Web research.

Between 2015 and 2021, his most popular works were:

  • Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (60 citations)
  • AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets. (53 citations)
  • Centroid-based Text Summarization through Compositionality of Word Embeddings (52 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Information retrieval, Natural language processing and Natural language. Recommender system is the subject of his research, which falls under World Wide Web. His Artificial intelligence study frequently intersects with other fields, such as Machine learning.

His Information retrieval study incorporates themes from Exploit, Sentiment analysis, Process and Granularity. His Natural language processing study combines topics from a wide range of disciplines, such as Diachronic analysis, Word, The Internet and Semantic change. His Natural language research is multidisciplinary, incorporating elements of Domain, Social network, Chatbot, Sentence and Alpha.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Content-based Recommender Systems: State of the Art and Trends

Pasquale Lops;Marco de Gemmis;Giovanni Semeraro.
Recommender Systems Handbook (2011)

2026 Citations

A comparative analysis of methods for pruning decision trees

F. Esposito;D. Malerba;G. Semeraro;J. Kay.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

680 Citations

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

Marco Degemmis;Pasquale Lops;Giovanni Semeraro.
User Modeling and User-adapted Interaction (2007)

237 Citations

Semantics-Aware Content-Based Recommender Systems.

Marco de Gemmis;Pasquale Lops;Cataldo Musto;Fedelucio Narducci.
Recommender Systems Handbook (2015)

211 Citations

Introducing Serendipity in a Content-Based Recommender System

L. Iaquinta;M. de Gemmis;P. Lops;G. Semeraro.
international conference hybrid intelligent systems (2008)

199 Citations

Integrating tags in a semantic content-based recommender

Marco de Gemmis;Pasquale Lops;Giovanni Semeraro;Pierpaolo Basile.
conference on recommender systems (2008)

179 Citations

An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model

Pierpaolo Basile;Annalina Caputo;Giovanni Semeraro.
international conference on computational linguistics (2014)

130 Citations

A Comparison of Lexicon-based Approaches for Sentiment Analysis of Microblog Posts.

Cataldo Musto;Giovanni Semeraro;Marco Polignano.
[email protected]*IA (2014)

127 Citations

MULTISTRATEGY LEARNING FOR DOCUMENT RECOGNITION

Floriana Esposito;Donato Malerba;Giovanni Semeraro.
Applied Artificial Intelligence (1994)

112 Citations

Multistrategy Theory Revision: Induction and Abductionin INTHELEX

Floriana Esposito;Giovanni Semeraro;Nicola Fanizzi;Stefano Ferilli.
Machine Learning (2000)

104 Citations

Best Scientists Citing Giovanni Semeraro

Floriana Esposito

Floriana Esposito

University of Bari Aldo Moro

Publications: 91

Jonathan J. Hull

Jonathan J. Hull

Independent Scientist / Consultant, US

Publications: 47

Donato Malerba

Donato Malerba

University of Bari Aldo Moro

Publications: 36

Berna Erol

Berna Erol

Ricoh (Japan)

Publications: 36

Francesco Ricci

Francesco Ricci

Free University of Bozen-Bolzano

Publications: 24

Jamey Graham

Jamey Graham

Ricoh (Japan)

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Peter E. Hart

Peter E. Hart

Ricoh (United States)

Publications: 17

Kurt Piersol

Kurt Piersol

Apple (United States)

Publications: 16

Jens Lehmann

Jens Lehmann

University of Bonn

Publications: 16

Dietmar Jannach

Dietmar Jannach

University of Klagenfurt

Publications: 15

Alexander Felfernig

Alexander Felfernig

Graz University of Technology

Publications: 15

John G. Breslin

John G. Breslin

National University of Ireland, Galway

Publications: 13

Luc De Raedt

Luc De Raedt

KU Leuven

Publications: 10

Peter Brusilovsky

Peter Brusilovsky

University of Pittsburgh

Publications: 10

Franca Garzotto

Franca Garzotto

Politecnico di Milano

Publications: 9

André C. P. L. F. de Carvalho

André C. P. L. F. de Carvalho

Universidade de São Paulo

Publications: 9

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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