H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 56 Citations 21,911 212 World Ranking 2003 National Ranking 37

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Quantum mechanics
  • Machine learning

Francesco Ricci spends much of his time researching Recommender system, World Wide Web, Collaborative filtering, Information retrieval and Process. His work carried out in the field of Recommender system brings together such families of science as User modeling, Personalization, Multimedia, Human–computer interaction and RSS. His World Wide Web study combines topics in areas such as Contextual information, User assistance, Ranking and Mobile computing.

His Collaborative filtering research includes themes of Active learning, Order, Data mining and Relevance. His research investigates the connection between Information retrieval and topics such as Data set that intersect with issues in k-nearest neighbors algorithm, Contextual variable and Context based. His biological study spans a wide range of topics, including Industrial organization and Artificial intelligence, Reinforcement learning.

His most cited work include:

  • Recommender Systems Handbook (2000 citations)
  • Introduction to Recommender Systems Handbook (1343 citations)
  • Context-Aware Recommender Systems (1238 citations)

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

His primary areas of study are Recommender system, World Wide Web, Information retrieval, Human–computer interaction and Collaborative filtering. Francesco Ricci combines subjects such as Quality, Data mining, Multimedia, Artificial intelligence and RSS with his study of Recommender system. His World Wide Web research integrates issues from Contextual information, User modeling, Mobile computing and Set.

His Information retrieval research is multidisciplinary, relying on both Similarity and Order. His work on Usability as part of general Human–computer interaction study is frequently linked to Graphical user interface, therefore connecting diverse disciplines of science. His Cold start study in the realm of Collaborative filtering connects with subjects such as Matrix decomposition.

He most often published in these fields:

  • Recommender system (58.47%)
  • World Wide Web (25.56%)
  • Information retrieval (17.25%)

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

  • Recommender system (58.47%)
  • World Wide Web (25.56%)
  • Human–computer interaction (15.34%)

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

The scientist’s investigation covers issues in Recommender system, World Wide Web, Human–computer interaction, Information retrieval and Preference elicitation. His study on Collaborative filtering is often connected to Group as part of broader study in Recommender system. His World Wide Web study combines topics from a wide range of disciplines, such as Decision-making, Preference learning, Mood, Decision support system and Usability.

The various areas that he examines in his Human–computer interaction study include Software development, Context model and Set. His Information retrieval research includes elements of RSS, Cluster analysis and Inverse reinforcement learning. His Preference elicitation research focuses on Cold start and how it connects with Feature and Feature based.

Between 2015 and 2021, his most popular works were:

  • A survey of active learning in collaborative filtering recommender systems (117 citations)
  • Alleviating the new user problem in collaborative filtering by exploiting personality information (72 citations)
  • Direct measurement of Kramers turnover with a levitated nanoparticle (70 citations)

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

  • Artificial intelligence
  • Quantum mechanics
  • Machine learning

His scientific interests lie mostly in Recommender system, World Wide Web, Information retrieval, Human–computer interaction and Group. His Recommender system research incorporates elements of Order, Decision-making and Usability. Within one scientific family, Francesco Ricci focuses on topics pertaining to Preference learning under World Wide Web, and may sometimes address concerns connected to Reinforcement learning and User modeling.

His Information retrieval research incorporates themes from Affect, Mood, Preference elicitation, Decision support system and Information and Communications Technology. He has included themes like Context model, Data mining and Software development in his Human–computer interaction study. His study in Task is interdisciplinary in nature, drawing from both Aggregation problem and Data science.

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.

Top Publications

Introduction to Recommender Systems Handbook

Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2011)

5976 Citations

Recommender Systems Handbook

Francesco Ricci;Lior Rokach;Bracha Shapira;Paul B. Kantor.
rsh (2010)

3437 Citations

Context-Aware Recommender Systems

Gediminas Adomavicius;Bamshad Mobasher;Francesco Ricci;Alexander Tuzhilin.
Ai Magazine (2011)

2620 Citations

Recommender Systems: Introduction and Challenges

Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2015)

1681 Citations

E-commerce and tourism

Hannes Werthner;Francesco Ricci.
Communications of The ACM (2004)

610 Citations

Group recommendations with rank aggregation and collaborative filtering

Linas Baltrunas;Tadas Makcinskas;Francesco Ricci.
conference on recommender systems (2010)

472 Citations

Mobile recommender systems.

Francesco Ricci.
Information Technology & Tourism (2010)

351 Citations

Matrix factorization techniques for context aware recommendation

Linas Baltrunas;Bernd Ludwig;Francesco Ricci.
conference on recommender systems (2011)

347 Citations

Context relevance assessment and exploitation in mobile recommender systems

Linas Baltrunas;Bernd Ludwig;Stefan Peer;Francesco Ricci.
ubiquitous computing (2012)

317 Citations

Improving recommender systems with adaptive conversational strategies

Tariq Mahmood;Francesco Ricci.
acm conference on hypertext (2009)

304 Citations

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

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Top Scientists Citing Francesco Ricci

Markus Schedl

Markus Schedl

Johannes Kepler University of Linz

Publications: 53

Dietmar Jannach

Dietmar Jannach

University of Klagenfurt

Publications: 50

Giovanni Semeraro

Giovanni Semeraro

University of Bari Aldo Moro

Publications: 45

Alexander Felfernig

Alexander Felfernig

Graz University of Technology

Publications: 41

Robin Burke

Robin Burke

University of Colorado Boulder

Publications: 31

Bamshad Mobasher

Bamshad Mobasher

DePaul University

Publications: 30

Iván Cantador

Iván Cantador

Autonomous University of Madrid

Publications: 28

Li Chen

Li Chen

Hong Kong Baptist University

Publications: 27

Alexander Tuzhilin

Alexander Tuzhilin

New York University

Publications: 27

Hannes Werthner

Hannes Werthner

TU Wien

Publications: 26

Lior Rokach

Lior Rokach

Ben-Gurion University of the Negev

Publications: 23

Barry Smyth

Barry Smyth

University College Dublin

Publications: 22

Hui Xiong

Hui Xiong

Rutgers, The State University of New Jersey

Publications: 20

Ulrike Gretzel

Ulrike Gretzel

University of Southern California

Publications: 19

Bracha Shapira

Bracha Shapira

Ben-Gurion University of the Negev

Publications: 19

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