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 4,237 116 World Ranking 7774 National Ranking 3647

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Yongfeng Zhang mainly focuses on Recommender system, Artificial intelligence, Collaborative filtering, Information retrieval and Machine learning. The Recommender system study combines topics in areas such as Learning to rank, Purchasing, Human–computer interaction and Taxonomy. His Artificial intelligence research incorporates themes from Ranking and Function.

As a part of the same scientific study, Yongfeng Zhang usually deals with the Collaborative filtering, concentrating on Sentiment analysis and frequently concerns with Phrase. His Information retrieval research includes elements of Feature learning, Markov chain and Personalization. In general Machine learning, his work in Leverage and Pruning is often linked to Product and User modeling linking many areas of study.

His most cited work include:

  • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (412 citations)
  • Sequential Recommendation with User Memory Networks (187 citations)
  • Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (151 citations)

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

His main research concerns Recommender system, Artificial intelligence, Information retrieval, Machine learning and Collaborative filtering. His work often combines Recommender system and Product studies. His Natural language processing research extends to the thematically linked field of Artificial intelligence.

His Information retrieval research is multidisciplinary, relying on both Ranking, Embedding and Feature learning. His Sentiment analysis study in the realm of Machine learning interacts with subjects such as Path. Yongfeng Zhang studies Web page, a branch of World Wide Web.

He most often published in these fields:

  • Recommender system (27.63%)
  • Artificial intelligence (17.76%)
  • Information retrieval (12.50%)

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

  • Recommender system (27.63%)
  • Artificial intelligence (17.76%)
  • Machine learning (10.53%)

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

The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Machine learning, Information retrieval and Vacancy defect. His Recommender system study focuses on Collaborative filtering in particular. Yongfeng Zhang combines subjects such as Microeconomics, Decision theory and Game theory with his study of Collaborative filtering.

His Machine learning research is multidisciplinary, incorporating perspectives in Variety, Space and Generative grammar. The study incorporates disciplines such as Ranking and E-commerce in addition to Information retrieval. His research investigates the connection between Vacancy defect and topics such as Density functional theory that intersect with issues in Ostwald ripening.

Between 2019 and 2021, his most popular works were:

  • Explainable Recommendation: A Survey and New Perspectives (110 citations)
  • Adaptive integral sliding mode control with payload sway reduction for 4-DOF tower crane systems (18 citations)
  • Fairness-Aware Explainable Recommendation over Knowledge Graphs (17 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

Yongfeng Zhang spends much of his time researching Recommender system, Artificial intelligence, Knowledge graph, Machine learning and Collaborative filtering. Yongfeng Zhang interconnects Quality and Operations research in the investigation of issues within Recommender system. His Artificial intelligence study typically links adjacent topics like Pipeline.

His study with Knowledge graph involves better knowledge in Information retrieval. His study in the field of Information seeking is also linked to topics like Construct. He has included themes like Ranking, Training set and Heuristic in his Collaborative filtering study.

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

Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang.
international acm sigir conference on research and development in information retrieval (2014)

507 Citations

Sequential Recommendation with User Memory Networks

Xu Chen;Hongteng Xu;Yongfeng Zhang;Jiaxi Tang.
web search and data mining (2018)

280 Citations

Explainable Recommendation: A Survey and New Perspectives

Yongfeng Zhang;Xu Chen.
Foundations and Trends in Information Retrieval (2020)

245 Citations

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

Yongfeng Zhang;Qingyao Ai;Xu Chen;W. Bruce Croft.
conference on information and knowledge management (2017)

200 Citations

Towards Conversational Search and Recommendation: System Ask, User Respond

Yongfeng Zhang;Xu Chen;Qingyao Ai;Liu Yang.
conference on information and knowledge management (2018)

180 Citations

Learning heterogeneous knowledge base embeddings for explainable recommendation

Qingyao Ai;Vahid Azizi;Xu Chen;Yongfeng Zhang.
Algorithms (2018)

142 Citations

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Yikun Xian;Zuohui Fu;S. Muthukrishnan;Gerard de Melo.
international acm sigir conference on research and development in information retrieval (2019)

126 Citations

Learning to Rank Features for Recommendation over Multiple Categories

Xu Chen;Zheng Qin;Yongfeng Zhang;Tao Xu.
international acm sigir conference on research and development in information retrieval (2016)

119 Citations

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

Liu Yang;Minghui Qiu;Chen Qu;Jiafeng Guo.
international acm sigir conference on research and development in information retrieval (2018)

97 Citations

Personalized Key Frame Recommendation

Xu Chen;Yongfeng Zhang;Qingyao Ai;Hongteng Xu.
international acm sigir conference on research and development in information retrieval (2017)

93 Citations

Best Scientists Citing Yongfeng Zhang

Xiangnan He

Xiangnan He

University of Science and Technology of China

Publications: 62

Tat-Seng Chua

Tat-Seng Chua

National University of Singapore

Publications: 48

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 37

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 34

Hongzhi Yin

Hongzhi Yin

University of Queensland

Publications: 27

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 26

Xing Xie

Xing Xie

Microsoft (United States)

Publications: 25

Liqiang Nie

Liqiang Nie

Shandong University

Publications: 25

Min Zhang

Min Zhang

Tsinghua University

Publications: 22

Lina Yao

Lina Yao

UNSW Sydney

Publications: 20

Richang Hong

Richang Hong

Hefei University of Technology

Publications: 14

Hui Xiong

Hui Xiong

Rutgers, The State University of New Jersey

Publications: 14

Gerhard Weikum

Gerhard Weikum

Max Planck Institute for Informatics

Publications: 13

Ji-Rong Wen

Ji-Rong Wen

Renmin University of China

Publications: 13

Sinno Jialin Pan

Sinno Jialin Pan

Nanyang Technological University

Publications: 12

Yixian Yang

Yixian Yang

Beijing University of Posts and Telecommunications

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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