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 32 Citations 3,997 190 World Ranking 7519 National Ranking 121

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Information retrieval

Tetsuya Sakai focuses on Information retrieval, Relevance, Data mining, Discriminative model and Information access. His biological study spans a wide range of topics, including Test and World Wide Web. His Relevance study combines topics in areas such as Question answering and Selection.

His Data mining research is multidisciplinary, relying on both Statistical hypothesis testing, Discounted cumulative gain and Rank correlation. His Discriminative model research includes elements of Ranking, Learning to rank and Web page. His Information access research focuses on subjects like Data science, which are linked to Automatic summarization.

His most cited work include:

  • Evaluating evaluation metrics based on the bootstrap (171 citations)
  • Information filtering apparatus for selecting predetermined article from plural articles to present selected article to user, and method therefore (130 citations)
  • Evaluating diversified search results using per-intent graded relevance (124 citations)

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

His primary scientific interests are in Information retrieval, Relevance, Artificial intelligence, Test and Data mining. In his articles, he combines various disciplines, including Information retrieval and Rank. Tetsuya Sakai combines subjects such as Ranking, Contrast and Discriminative model with his study of Relevance.

His Artificial intelligence research includes themes of Machine learning, Conversation, Speech recognition and Natural language processing. Analysis of variance is closely connected to Sample size determination in his research, which is encompassed under the umbrella topic of Test. His Data mining study frequently draws connections to adjacent fields such as Discounted cumulative gain.

He most often published in these fields:

  • Information retrieval (48.11%)
  • Relevance (23.11%)
  • Artificial intelligence (18.18%)

What were the highlights of his more recent work (between 2018-2021)?

  • Information retrieval (48.11%)
  • Relevance (23.11%)
  • Artificial intelligence (18.18%)

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

Tetsuya Sakai spends much of his time researching Information retrieval, Relevance, Artificial intelligence, Test and Natural language processing. The various areas that Tetsuya Sakai examines in his Information retrieval study include Data warehouse and Data set. His Relevance research incorporates elements of Crowdsourcing, Learning to rank, Preference and Presentation.

His work on Natural language generation as part of general Artificial intelligence research is often related to Lyrics, thus linking different fields of science. His Test study integrates concerns from other disciplines, such as Analysis of variance, Sample size determination, Replication, Statistical power and Set. His Natural language processing study incorporates themes from Domain, Poetry and Table.

Between 2018 and 2021, his most popular works were:

  • Personalized Reason Generation for Explainable Song Recommendation (14 citations)
  • The SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019) (8 citations)
  • Which Diversity Evaluation Measures Are "Good"? (8 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Information retrieval, Relevance, Data science, Open source and Clef. He connects Information retrieval with Hierarchy in his research. His Relevance study combines topics from a wide range of disciplines, such as Personalization, Recommender system, Natural language generation, Natural language and Pattern recognition.

The study incorporates disciplines such as Context and Generalizability theory in addition to Clef. Tetsuya Sakai focuses mostly in the field of Replication, narrowing it down to matters related to Test and, in some cases, Software engineering, Set and Section. His Preference research is multidisciplinary, incorporating perspectives in Learning to rank, Pairwise comparison and Data set.

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

Evaluating evaluation metrics based on the bootstrap

Tetsuya Sakai.
international acm sigir conference on research and development in information retrieval (2006)

230 Citations

Information filtering apparatus for selecting predetermined article from plural articles to present selected article to user, and method therefore

Kazuo Sumita;Tetsuya Sakai;Masahiro Kajiura;Kenji Ono.
(1996)

175 Citations

Evaluating diversified search results using per-intent graded relevance

Tetsuya Sakai;Ruihua Song.
international acm sigir conference on research and development in information retrieval (2011)

167 Citations

Alternatives to Bpref

Tetsuya Sakai.
international acm sigir conference on research and development in information retrieval (2007)

159 Citations

On the reliability of information retrieval metrics based on graded relevance

Tetsuya Sakai.
Information Processing and Management (2007)

142 Citations

On information retrieval metrics designed for evaluation with incomplete relevance assessments

Tetsuya Sakai;Noriko Kando.
Information Retrieval (2008)

129 Citations

Flexible pseudo-relevance feedback via selective sampling

Tetsuya Sakai;Toshihiko Manabe;Makoto Koyama.
ACM Transactions on Asian Language Information Processing (2005)

117 Citations

Generic summaries for indexing in information retrieval

Tetsuya Sakai;Karen Sparck-Jones.
international acm sigir conference on research and development in information retrieval (2001)

97 Citations

Overview of the NTCIR-9 INTENT Task

Ruihua Song;Min Zhang;Tetsuya Sakai;Makoto P. Kato.
NTCIR (2011)

97 Citations

Statistical reform in information retrieval

Tetsuya Sakai.
international acm sigir conference on research and development in information retrieval (2014)

90 Citations

Best Scientists Citing Tetsuya Sakai

Min Zhang

Min Zhang

Tsinghua University

Publications: 37

Alistair Moffat

Alistair Moffat

University of Melbourne

Publications: 35

Charles L. A. Clarke

Charles L. A. Clarke

University of Waterloo

Publications: 29

Jimmy Lin

Jimmy Lin

University of Waterloo

Publications: 21

Mark Sanderson

Mark Sanderson

RMIT University

Publications: 19

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 18

Gareth J. F. Jones

Gareth J. F. Jones

Leeds Beckett University

Publications: 17

Leif Azzopardi

Leif Azzopardi

University of Strathclyde

Publications: 16

Mark J. Stefik

Mark J. Stefik

Palo Alto Research Center

Publications: 15

Craig Macdonald

Craig Macdonald

University of Glasgow

Publications: 15

Jian-Yun Nie

Jian-Yun Nie

University of Montreal

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James Allan

James Allan

University of Massachusetts Amherst

Publications: 15

Iadh Ounis

Iadh Ounis

University of Glasgow

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W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 14

Justin Zobel

Justin Zobel

University of Melbourne

Publications: 14

Douglas W. Oard

Douglas W. Oard

University of Maryland, College Park

Publications: 13

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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