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 31 Citations 4,530 78 World Ranking 7828 National Ranking 3677

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Information retrieval, Artificial intelligence, Machine learning, Data mining and Search engine are his primary areas of study. His Information retrieval research includes themes of Language model, Probabilistic analysis of algorithms, STREAMS, Cluster analysis and Text mining. The study of Artificial intelligence is intertwined with the study of Algorithm in a number of ways.

In general Machine learning, his work in Recommender system is often linked to Front page, Evaluation methods and Multi-armed bandit linking many areas of study. His Data mining research is multidisciplinary, relying on both Click model and Statistics, Pairwise comparison. His Search engine study integrates concerns from other disciplines, such as Information needs and Selection bias.

His most cited work include:

  • Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (351 citations)
  • Have things changed now?: an empirical study of bug characteristics in modern open source software (235 citations)
  • Learn from web search logs to organize search results (192 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Information retrieval, Machine learning, Learning to rank and Ranking. The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing and Pattern recognition. His work deals with themes such as Language model and Data mining, which intersect with Information retrieval.

His Data mining research integrates issues from Statistics and Cluster analysis. His Machine learning research focuses on Pairwise comparison and how it connects with Regression. In his study, Recommender system is strongly linked to Scalability, which falls under the umbrella field of Learning to rank.

He most often published in these fields:

  • Artificial intelligence (42.86%)
  • Information retrieval (40.66%)
  • Machine learning (31.87%)

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

  • Artificial intelligence (42.86%)
  • Learning to rank (25.27%)
  • Machine learning (31.87%)

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

Xuanhui Wang focuses on Artificial intelligence, Learning to rank, Machine learning, Ranking and Artificial neural network. His work deals with themes such as Ensemble learning, Boosting, Distributed representation and Theoretical computer science, which intersect with Learning to rank. His work in the fields of Machine learning, such as Ranking, intersects with other areas such as Gradient boosting.

His Ranking research incorporates elements of Leverage, Noise, Inference, Tree and Semantic matching. His research investigates the link between Leverage and topics such as Information retrieval that cross with problems in Language model. He combines subjects such as User experience design, Interpretability and Generalized additive model with his study of Artificial neural network.

Between 2018 and 2021, his most popular works were:

  • Estimating Position Bias without Intrusive Interventions (50 citations)
  • TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (48 citations)
  • Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks (25 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Xuanhui Wang mainly investigates Ranking, Artificial intelligence, Learning to rank, Machine learning and Relevance. In his study, which falls under the umbrella issue of Ranking, Tree is strongly linked to Deep learning. His Learning to rank study deals with the bigger picture of Information retrieval.

His research on Machine learning often connects related areas such as Semantic matching. The concepts of his Relevance study are interwoven with issues in Ranking, Pairwise comparison, Econometrics and Search engine. His Leverage study incorporates themes from Language model, Scalability, Recommender system and Question answering.

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

Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

Lihong Li;Wei Chu;John Langford;Xuanhui Wang.
web search and data mining (2011)

475 Citations

Have things changed now?: an empirical study of bug characteristics in modern open source software

Zhenmin Li;Lin Tan;Xuanhui Wang;Shan Lu.
Proceedings of the 1st workshop on Architectural and system support for improving software dependability (2006)

296 Citations

Mining correlated bursty topic patterns from coordinated text streams

Xuanhui Wang;ChengXiang Zhai;Xiao Hu;Richard Sproat.
knowledge discovery and data mining (2007)

285 Citations

Learn from web search logs to organize search results

Xuanhui Wang;ChengXiang Zhai.
international acm sigir conference on research and development in information retrieval (2007)

266 Citations

Probabilistic dyadic data analysis with local and global consistency

Deng Cai;Xuanhui Wang;Xiaofei He.
international conference on machine learning (2009)

190 Citations

Mining term association patterns from search logs for effective query reformulation

Xuanhui Wang;ChengXiang Zhai.
conference on information and knowledge management (2008)

175 Citations

Locality preserving nonnegative matrix factorization

Deng Cai;Xiaofei He;Xuanhui Wang;Hujun Bao.
international joint conference on artificial intelligence (2009)

162 Citations

Bug characteristics in open source software

Lin Tan;Chen Liu;Zhenmin Li;Xuanhui Wang.
Empirical Software Engineering (2014)

159 Citations

Language Model Information Retrieval with Document Expansion

Tao Tao;Xuanhui Wang;Qiaozhu Mei;ChengXiang Zhai.
language and technology conference (2006)

153 Citations

A study of methods for negative relevance feedback

Xuanhui Wang;Hui Fang;ChengXiang Zhai.
international acm sigir conference on research and development in information retrieval (2008)

151 Citations

Best Scientists Citing Xuanhui Wang

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 48

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 41

ChengXiang Zhai

ChengXiang Zhai

University of Illinois at Urbana-Champaign

Publications: 38

Thorsten Joachims

Thorsten Joachims

Cornell University

Publications: 31

Yi Chang

Yi Chang

Jilin University

Publications: 22

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 17

Marc Najork

Marc Najork

Google (United States)

Publications: 16

Donald Metzler

Donald Metzler

Google (United States)

Publications: 15

Hang Li

Hang Li

ByteDance

Publications: 15

Shan Lu

Shan Lu

University of Chicago

Publications: 14

Mounia Lalmas

Mounia Lalmas

Spotify

Publications: 14

Deng Cai

Deng Cai

Zhejiang University

Publications: 14

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 13

Qiaozhu Mei

Qiaozhu Mei

University of Michigan–Ann Arbor

Publications: 13

Dawei Yin

Dawei Yin

Baidu (China)

Publications: 12

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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