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 67 Citations 30,151 170 World Ranking 1022 National Ranking 610

Research.com Recognitions

Awards & Achievements

2013 - ACM Fellow For contributions to the theory and practice of information retrieval.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Information retrieval

Stephen Robertson mainly focuses on Information retrieval, Relevance, Artificial intelligence, Data mining and Relevance feedback. The study incorporates disciplines such as Ranking, Probabilistic logic and Statistical model in addition to Information retrieval. His work blends Relevance and Weighting studies together.

In the subject of general Artificial intelligence, his work in Language model is often linked to Basis, thereby combining diverse domains of study. His Data mining study incorporates themes from Adaptive filter and Ranking. His work in Okapi BM25 addresses issues such as Term Discrimination, which are connected to fields such as Partition.

His most cited work include:

  • Okapi at TREC (2262 citations)
  • Relevance weighting of search terms (1816 citations)
  • Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval (1119 citations)

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

His primary areas of investigation include Information retrieval, Relevance, Artificial intelligence, Data mining and Probabilistic logic. His work on Human–computer information retrieval, Ranking and Search engine indexing as part of general Information retrieval study is frequently linked to Relevance feedback and Weighting, bridging the gap between disciplines. His biological study spans a wide range of topics, including Divergence-from-randomness model, Information needs, Okapi BM25, Query expansion and Ranking.

His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Natural language processing. His study explores the link between Data mining and topics such as Adaptive filter that cross with problems in Routing. His research on Probabilistic logic often connects related areas such as Statistical model.

He most often published in these fields:

  • Information retrieval (63.55%)
  • Relevance (29.91%)
  • Artificial intelligence (22.43%)

What were the highlights of his more recent work (between 2007-2020)?

  • Information retrieval (63.55%)
  • Relevance (29.91%)
  • Artificial intelligence (22.43%)

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

Stephen Robertson focuses on Information retrieval, Relevance, Artificial intelligence, Data mining and Machine learning. His work carried out in the field of Information retrieval brings together such families of science as Task and Information needs. His Relevance research includes elements of Ranking, Probabilistic logic, Ranking and Probabilistic relevance model.

His Divergence-from-randomness model and Language model study in the realm of Artificial intelligence interacts with subjects such as Relevance feedback and Limited resources. He has included themes like Learning to rank, Stochastic modelling and Mixed model in his Data mining study. Stephen Robertson interconnects Okapi BM25 and Pattern recognition in the investigation of issues within Machine learning.

Between 2007 and 2020, his most popular works were:

  • The Probabilistic Relevance Framework (952 citations)
  • Selecting good expansion terms for pseudo-relevance feedback (362 citations)
  • SoftRank: optimizing non-smooth rank metrics (257 citations)

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

  • Statistics
  • Artificial intelligence
  • Information retrieval

Stephen Robertson mainly investigates Information retrieval, Relevance, Data mining, Probabilistic logic and Ranking. In general Information retrieval, his work in Search engine is often linked to Context linking many areas of study. His Relevance research includes themes of Document retrieval and Formative assessment.

Stephen Robertson works mostly in the field of Data mining, limiting it down to topics relating to Learning to rank and, in certain cases, Divergence-from-randomness model and Probabilistic relevance model, as a part of the same area of interest. His work in Probabilistic logic covers topics such as Collaborative filtering which are related to areas like Heuristics, Matching, Bayesian inference and Okapi BM25. His work on Language model, Support vector machine and Unsupervised learning as part of his general Artificial intelligence study is frequently connected to Supervised learning, thereby bridging the divide between different branches of 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.

Best Publications

Okapi at TREC

Stephen E. Robertson;Steve Walker;Susan Jones;Micheline Hancock-Beaulieu.
text retrieval conference (1994)

3758 Citations

Relevance weighting of search terms

Stephen Robertson;K. Sparck Jones.
Journal of the Association for Information Science and Technology (1976)

2972 Citations

Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval

S. E. Robertson;S. Walker.
international acm sigir conference on research and development in information retrieval (1994)

1781 Citations

A probabilistic model of information retrieval: development and comparative experiments

K. Sparck Jones;S. Walker;S. E. Robertson;S. E. Robertson.
Information Processing and Management (2000)

1497 Citations

The Probabilistic Relevance Framework

Stephen Robertson;Hugo Zaragoza.
(2009)

1472 Citations

Understanding inverse document frequency: on theoretical arguments for IDF

Stephen Robertson.
Journal of Documentation (2004)

1444 Citations

The probability ranking principle in IR

S. E. Robertson.
Journal of Documentation (1997)

1304 Citations

Simple BM25 extension to multiple weighted fields

Stephen Robertson;Hugo Zaragoza;Michael Taylor.
conference on information and knowledge management (2004)

703 Citations

Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive.

Stephen E. Robertson;Steve Walker;Micheline Hancock-Beaulieu.
text retrieval conference (1998)

536 Citations

Okapi/Keenbow at TREC-8.

Stephen E. Robertson;Steve Walker.
text retrieval conference (1999)

516 Citations

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