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 52 Citations 14,039 170 World Ranking 2622 National Ranking 252

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Xiangnan He mainly investigates Artificial intelligence, Machine learning, Recommender system, Collaborative filtering and Embedding. His work in the fields of Artificial intelligence, such as Deep learning, Artificial neural network and Sequence learning, overlaps with other areas such as Matrix decomposition. Missing data is closely connected to Data mining in his research, which is encompassed under the umbrella topic of Machine learning.

His Recommender system study is focused on Information retrieval in general. He combines subjects such as Interpretability, Theoretical computer science, Quantization and Quantization with his study of Collaborative filtering. His studies in Embedding integrate themes in fields like Tree, Global network, Robustness and Decision tree.

His most cited work include:

  • Neural Collaborative Filtering (1578 citations)
  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback (507 citations)
  • Neural Factorization Machines for Sparse Predictive Analytics (427 citations)

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

Xiangnan He spends much of his time researching Artificial intelligence, Recommender system, Machine learning, Information retrieval and Collaborative filtering. His research in the fields of Deep learning, Artificial neural network, Embedding and Representation overlaps with other disciplines such as Matrix decomposition. In his study, Bayesian probability is inextricably linked to Ranking, which falls within the broad field of Recommender system.

His work on Feature is typically connected to Quality, Sampling, Popularity and Generalization as part of general Machine learning study, connecting several disciplines of science. The Relevance research he does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Raw data, therefore creating a link between diverse domains of science. His work is dedicated to discovering how Collaborative filtering, Theoretical computer science are connected with Feature learning and Node and other disciplines.

He most often published in these fields:

  • Artificial intelligence (45.38%)
  • Recommender system (36.55%)
  • Machine learning (29.72%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (45.38%)
  • Recommender system (36.55%)
  • Machine learning (29.72%)

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

His main research concerns Artificial intelligence, Recommender system, Machine learning, Information retrieval and Collaborative filtering. His Artificial intelligence study typically links adjacent topics like Natural language processing. He usually deals with Recommender system and limits it to topics linked to Pairwise comparison and Bilinear interpolation.

His Leverage and Interpretability study in the realm of Machine learning interacts with subjects such as Quality, Popularity and Sampling. Xiangnan He has researched Information retrieval in several fields, including Cover and Social network. His Collaborative filtering research is multidisciplinary, relying on both Ranking, Embedding, Theoretical computer science and Task analysis.

Between 2019 and 2021, his most popular works were:

  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (120 citations)
  • Generative Adversarial Active Learning for Unsupervised Outlier Detection (65 citations)
  • Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (46 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

Xiangnan He mostly deals with Recommender system, Artificial intelligence, Information retrieval, Machine learning and Collaborative filtering. His Recommender system research integrates issues from Visualization, Key and Natural language. His work on Deep learning, Reinforcement learning and Attention network as part of general Artificial intelligence research is often related to Granularity, thus linking different fields of science.

His study in the field of Artificial neural network and Overfitting is also linked to topics like Quality and Bipartite graph. His Collaborative filtering study integrates concerns from other disciplines, such as Embedding, Task analysis and Categorical variable. His Embedding study incorporates themes from Interpretability and Theoretical computer 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

Neural Collaborative Filtering

Xiangnan He;Lizi Liao;Hanwang Zhang;Liqiang Nie.
the web conference (2017)

1342 Citations

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua.
international acm sigir conference on research and development in information retrieval (2016)

484 Citations

Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie.
international acm sigir conference on research and development in information retrieval (2017)

364 Citations

TriRank: Review-aware Explainable Recommendation by Modeling Aspects

Xiangnan He;Tao Chen;Min-Yen Kan;Xiao Chen.
conference on information and knowledge management (2015)

299 Citations

Neural Factorization Machines for Sparse Predictive Analytics

Xiangnan He;Tat-Seng Chua.
international acm sigir conference on research and development in information retrieval (2017)

294 Citations

Neural Graph Collaborative Filtering

Xiang Wang;Xiangnan He;Meng Wang;Fuli Feng.
international acm sigir conference on research and development in information retrieval (2019)

266 Citations

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

Jun Xiao;Hao Ye;Xiangnan He;Hanwang Zhang.
international joint conference on artificial intelligence (2017)

244 Citations

KGAT: Knowledge Graph Attention Network for Recommendation

Xiang Wang;Xiangnan He;Yixin Cao;Meng Liu.
knowledge discovery and data mining (2019)

222 Citations

NAIS: Neural Attentive Item Similarity Model for Recommendation

Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu.
IEEE Transactions on Knowledge and Data Engineering (2018)

205 Citations

Attributed Social Network Embedding

Lizi Liao;Xiangnan He;Hanwang Zhang;Tat-Seng Chua.
IEEE Transactions on Knowledge and Data Engineering (2018)

204 Citations

Best Scientists Citing Xiangnan He

Liqiang Nie

Liqiang Nie

Shandong University

Publications: 64

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 57

Xing Xie

Xing Xie

Microsoft (United States)

Publications: 48

Yongfeng Zhang

Yongfeng Zhang

Rutgers, The State University of New Jersey

Publications: 46

Hongzhi Yin

Hongzhi Yin

University of Queensland

Publications: 44

Tat-Seng Chua

Tat-Seng Chua

National University of Singapore

Publications: 42

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 41

Lina Yao

Lina Yao

UNSW Sydney

Publications: 33

Richang Hong

Richang Hong

Hefei University of Technology

Publications: 33

Zi Huang

Zi Huang

University of Queensland

Publications: 33

Zhou Zhao

Zhou Zhao

Zhejiang University

Publications: 32

James Caverlee

James Caverlee

Texas A&M University

Publications: 29

Meng Wang

Meng Wang

Hefei University of Technology

Publications: 28

Xia Hu

Xia Hu

Rice University

Publications: 28

Fuzhen Zhuang

Fuzhen Zhuang

Beihang University

Publications: 27

Enhong Chen

Enhong Chen

University of Science and Technology of China

Publications: 27

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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