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 33 Citations 7,109 68 World Ranking 6923 National Ranking 3282

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • World Wide Web
  • Information retrieval

Filip Radlinski mainly focuses on Artificial intelligence, Information retrieval, Machine learning, Ranking and Ranking. His research in Artificial intelligence tackles topics such as Information needs which are related to areas like Natural language processing. His work is connected to Search engine, Web search query and Relevance, as a part of Information retrieval.

His research integrates issues of Training set and Data mining in his study of Search engine. Learning to rank is the focus of his Machine learning research. His Ranking research focuses on subjects like Support vector machine, which are linked to Active learning, Computational learning theory and Online machine learning.

His most cited work include:

  • A support vector method for optimizing average precision (617 citations)
  • Query chains: learning to rank from implicit feedback (496 citations)
  • Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search (492 citations)

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

His primary areas of investigation include Information retrieval, Ranking, Search engine, Relevance and Artificial intelligence. His Information retrieval study combines topics in areas such as World Wide Web and Data mining. His work in the fields of Ranking, such as Ranking SVM, overlaps with other areas such as Context.

His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Contrast and Natural language processing. His Ranking research includes elements of Search analytics, Web search engine and Pairwise comparison. His Web search query research is multidisciplinary, incorporating elements of Query expansion and Eye tracking.

He most often published in these fields:

  • Information retrieval (50.00%)
  • Ranking (26.14%)
  • Search engine (25.00%)

What were the highlights of his more recent work (between 2016-2021)?

  • Information retrieval (50.00%)
  • Recommender system (10.23%)
  • Human–computer interaction (4.55%)

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

Filip Radlinski mainly investigates Information retrieval, Recommender system, Human–computer interaction, World Wide Web and Preference. His study in the fields of Search engine and Cognitive models of information retrieval under the domain of Information retrieval overlaps with other disciplines such as Panel discussion and Focus. Filip Radlinski usually deals with Recommender system and limits it to topics linked to Natural language and Knowledge base and Ranking.

The study incorporates disciplines such as Embedding and Forcing in addition to Human–computer interaction. His Preference elicitation study in the realm of Preference connects with subjects such as Machine learning, Factor, Optimization problem and Fraction. Filip Radlinski focuses mostly in the field of Machine learning, narrowing it down to matters related to Artificial intelligence and, in some cases, Dialog box.

Between 2016 and 2021, his most popular works were:

  • A Theoretical Framework for Conversational Search (162 citations)
  • TREC Complex Answer Retrieval Overview. (74 citations)
  • Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences (38 citations)

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

  • Artificial intelligence
  • World Wide Web
  • Information retrieval

Filip Radlinski mostly deals with Human–computer interaction, Preference elicitation, Preference, Recommender system and Natural. A majority of his Preference elicitation research is a blend of other scientific areas, such as Optimization problem, Set, Factor and Machine learning. He regularly links together related areas like Artificial intelligence in his Optimization problem studies.

His Artificial intelligence research incorporates elements of Information needs and Natural language processing. His research in the fields of Collaborative filtering overlaps with other disciplines such as Transparency and User modeling. Natural combines with fields such as Variety, Small set, Chatbot, Measure and Space in his research.

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

A support vector method for optimizing average precision

Yisong Yue;Thomas Finley;Filip Radlinski;Thorsten Joachims.
international acm sigir conference on research and development in information retrieval (2007)

802 Citations

Query chains: learning to rank from implicit feedback

Filip Radlinski;Thorsten Joachims.
knowledge discovery and data mining (2005)

652 Citations

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

Thorsten Joachims;Laura Granka;Bing Pan;Helene Hembrooke.
ACM Transactions on Information Systems (2007)

649 Citations

Learning diverse rankings with multi-armed bandits

Filip Radlinski;Robert Kleinberg;Thorsten Joachims.
international conference on machine learning (2008)

513 Citations

How does clickthrough data reflect retrieval quality

Filip Radlinski;Madhu Kurup;Thorsten Joachims.
conference on information and knowledge management (2008)

372 Citations

Improving personalized web search using result diversification

Filip Radlinski;Susan Dumais.
international acm sigir conference on research and development in information retrieval (2006)

325 Citations

Personalizing web search using long term browsing history

Nicolaas Matthijs;Filip Radlinski.
web search and data mining (2011)

246 Citations

Search Engines that Learn from Implicit Feedback

T. Joachims;F. Radlinski.
IEEE Computer (2007)

223 Citations

Active exploration for learning rankings from clickthrough data

Filip Radlinski;Thorsten Joachims.
knowledge discovery and data mining (2007)

206 Citations

Large-scale validation and analysis of interleaved search evaluation

Olivier Chapelle;Thorsten Joachims;Filip Radlinski;Yisong Yue.
ACM Transactions on Information Systems (2012)

180 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Filip Radlinski

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 99

Ryen W. White

Ryen W. White

Microsoft (United States)

Publications: 56

Thorsten Joachims

Thorsten Joachims

Cornell University

Publications: 49

Tie-Yan Liu

Tie-Yan Liu

Microsoft (United States)

Publications: 40

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 35

Nick Craswell

Nick Craswell

Microsoft (United States)

Publications: 32

Yi Chang

Yi Chang

Jilin University

Publications: 30

Dawei Song

Dawei Song

Beijing Institute of Technology

Publications: 30

Hang Li

Hang Li

ByteDance

Publications: 27

Paul N. Bennett

Paul N. Bennett

Microsoft (United States)

Publications: 27

Susan T. Dumais

Susan T. Dumais

Microsoft (United States)

Publications: 27

Jiafeng Guo

Jiafeng Guo

Chinese Academy of Sciences

Publications: 26

Gerhard Weikum

Gerhard Weikum

Max Planck Institute for Informatics

Publications: 24

Yisong Yue

Yisong Yue

California Institute of Technology

Publications: 24

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 24

Csaba Szepesvári

Csaba Szepesvári

University of Alberta

Publications: 22

Something went wrong. Please try again later.