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 51 Citations 9,203 265 World Ranking 2769 National Ranking 161

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • The Internet
  • Information retrieval

Information retrieval, Artificial intelligence, World Wide Web, Machine learning and Data mining are his primary areas of study. In general Information retrieval, his work in Ranking and Query expansion is often linked to Diversification and Track linking many areas of study. His research integrates issues of Ranking and Heuristics in his study of Ranking.

As a part of the same scientific family, Craig Macdonald mostly works in the field of Query expansion, focusing on Web query classification and, on occasion, Sargable and Query optimization. He interconnects Baseline and Natural language processing in the investigation of issues within Artificial intelligence. He has included themes like Text Retrieval Conference and Information seeking in his World Wide Web study.

His most cited work include:

  • Exploiting query reformulations for web search result diversification (365 citations)
  • Overview of the TREC 2006 Blog Track (246 citations)
  • Terrier information retrieval platform (237 citations)

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

Craig Macdonald focuses on Information retrieval, Artificial intelligence, World Wide Web, Ranking and Search engine. His Information retrieval research focuses on subjects like Social media, which are linked to Crowdsourcing. His Artificial intelligence study combines topics in areas such as Machine learning, Recommender system and Natural language processing.

His study of Blogosphere is a part of World Wide Web. His multidisciplinary approach integrates Ranking and Voting in his work. His biological study deals with issues like Query optimization, which deal with fields such as Sargable.

He most often published in these fields:

  • Information retrieval (55.41%)
  • Artificial intelligence (20.33%)
  • World Wide Web (17.70%)

What were the highlights of his more recent work (between 2017-2021)?

  • Information retrieval (55.41%)
  • Artificial intelligence (20.33%)
  • Machine learning (13.11%)

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

His primary areas of investigation include Information retrieval, Artificial intelligence, Machine learning, Recommender system and Classifier. Learning to rank and Ranking are subfields of Information retrieval in which his conducts study. His Artificial intelligence research is multidisciplinary, relying on both Ranking and Natural language processing.

Craig Macdonald combines subjects such as Feedback loop and Bayesian probability with his study of Machine learning. His Recommender system research incorporates elements of Recurrent neural network and Leverage. His studies in Classifier integrate themes in fields like Active learning, Information sensitivity, Personally identifiable information and Training set.

Between 2017 and 2021, his most popular works were:

  • Using word embeddings in Twitter election classification (66 citations)
  • A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation (43 citations)
  • Regional Sentiment Bias in Social Media Reporting During Crises (10 citations)

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

  • Artificial intelligence
  • The Internet
  • Machine learning

His primary areas of study are Artificial intelligence, Information retrieval, Machine learning, Social media and Recommender system. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Personally identifiable information and Natural language processing. Craig Macdonald brings together Information retrieval and Multiple days to produce work in his papers.

His work deals with themes such as Classifier, Representation, Inference and Bayesian inference, which intersect with Machine learning. His biological study spans a wide range of topics, including Ranking, Recurrent neural network and Human–computer interaction. Craig Macdonald has researched Ranking in several fields, including Predictive modelling, Leverage and Selection.

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

Terrier information retrieval platform

Iadh Ounis;Gianni Amati;Vassilis Plachouras;Ben He.
european conference on information retrieval (2005)

749 Citations

Exploiting query reformulations for web search result diversification

Rodrygo L.T. Santos;Craig Macdonald;Iadh Ounis.
the web conference (2010)

500 Citations

Overview of the TREC-2008 Blog Track

Iadh Ounis;Craig Macdonald;Ian Soboroff.
text retrieval conference (2008)

443 Citations

Overview of the TREC 2006 Blog Track

Iadh Ounis;Craig Macdonald;Maarten de Rijke;Gilad Mishne.
text retrieval conference (2006)

441 Citations

Overview of the TREC-2007 Blog Track

Craig Macdonald;Iadh Ounis;Ian Soboroff.
text retrieval conference (2007)

322 Citations

Voting for candidates: adapting data fusion techniques for an expert search task

Craig Macdonald;Iadh Ounis.
conference on information and knowledge management (2006)

312 Citations

The TREC Blogs06 Collection: Creating and Analysing a Blog Test Collection

C. Macdonald;I. Ounis.
(2006)

249 Citations

Overview of the TREC-2011 Microblog Track

Iadh Ounis;Craig Macdonald;Jimmy Lin;Ian Soboroff.
text retrieval conference (2011)

220 Citations

Overview of the TREC-2009 Blog Track

Craig Macdonald;Iadh Ounis;Ian Soboroff.
text retrieval conference (2009)

203 Citations

Overview of the TREC-2012 Microblog Track.

Ian Soboroff;Iadh Ounis;Craig Macdonald;Jimmy J. Lin.
text retrieval conference (2012)

139 Citations

Best Scientists Citing Craig Macdonald

Jimmy Lin

Jimmy Lin

University of Waterloo

Publications: 74

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 70

Fabio Crestani

Fabio Crestani

Universita della Svizzera Italiana

Publications: 46

Krisztian Balog

Krisztian Balog

University of Stavanger

Publications: 35

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 31

Tetsuya Sakai

Tetsuya Sakai

Waseda University

Publications: 26

Joemon M. Jose

Joemon M. Jose

University of Glasgow

Publications: 24

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 24

Alistair Moffat

Alistair Moffat

University of Melbourne

Publications: 23

Djoerd Hiemstra

Djoerd Hiemstra

Radboud University Nijmegen

Publications: 19

Iadh Ounis

Iadh Ounis

University of Glasgow

Publications: 19

Gareth JF Jones

Gareth JF Jones

Dublin City University

Publications: 18

ChengXiang Zhai

ChengXiang Zhai

University of Illinois at Urbana-Champaign

Publications: 16

Charles L. A. Clarke

Charles L. A. Clarke

University of Waterloo

Publications: 15

Jiafeng Guo

Jiafeng Guo

Chinese Academy of Sciences

Publications: 15

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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