D-Index & Metrics Best Publications

Nicholas Jing Yuan

Microsoft (United States)
United States

D-Index & Metrics 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.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 31 Citations 5,085 55 World Ranking 9729 National Ranking 4415

Overview

What is he best known for?

The fields of study he is best known for:

Artificial intelligence
Machine learning
Data mining

Data mining, Collaborative filtering, Key, Variety and Set are his primary areas of study. His biological study spans a wide range of topics, including Deep learning and Knowledge base. His Deep learning research also covers Machine learning and Artificial intelligence studies.

In Machine learning, Nicholas Jing Yuan works on issues like Inference, which are connected to Social network. His Key study incorporates themes from Search engine indexing and Cluster analysis. His Recommender system research is multidisciplinary, incorporating elements of Embedding, Semantics and Unstructured data.

His most cited work include:

Collaborative Knowledge Base Embedding for Recommender Systems (553 citations)
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (301 citations)
Discovering Urban Functional ZonesUsing Latent Activity Trajectories (223 citations)

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

Nicholas Jing Yuan spends much of his time researching Artificial intelligence, Data mining, Information retrieval, Machine learning and Collaborative filtering. In general Artificial intelligence study, his work on Deep learning and Knowledge graph often relates to the realm of Construct, thereby connecting several areas of interest. His work carried out in the field of Data mining brings together such families of science as Smart card, Cluster analysis and Feature vector.

His studies examine the connections between Machine learning and genetics, as well as such issues in Social network, with regards to Data science, Transfer of learning and Information needs. His Collaborative filtering research is within the category of Recommender system. His Recommender system research includes themes of Embedding, Knowledge base and Unstructured data.

He most often published in these fields:

Artificial intelligence (24.24%)
Data mining (22.73%)
Information retrieval (19.70%)

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

Natural language generation (6.06%)
Information retrieval (19.70%)
Sequence (6.06%)

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

His primary areas of study are Natural language generation, Information retrieval, Sequence, Artificial intelligence and Focus. He has researched Information retrieval in several fields, including Entity linking and Coherence. Artificial intelligence is closely attributed to Natural language processing in his work.

His research in Natural language processing intersects with topics in Word, Encoding and Knowledge graph. His Focus research incorporates elements of Coherence, Plan, Structure and Sentence.

Between 2018 and 2021, his most popular works were:

Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering (9 citations)
Integrating Graph Contextualized Knowledge into Pre-trained Language Models (7 citations)
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking (2 citations)

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

Artificial intelligence
Machine learning
World Wide Web

Nicholas Jing Yuan focuses on Information retrieval, Knowledge graph, Knowledge representation and reasoning, Language model and Theoretical computer science. His work carried out in the field of Information retrieval brings together such families of science as Entity linking and Coherence. As part of his studies on Knowledge graph, he often connects relevant subjects like Transformer.

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

Collaborative Knowledge Base Embedding for Recommender Systems

Fuzheng Zhang;Nicholas Jing Yuan;Defu Lian;Xing Xie.
knowledge discovery and data mining (2016)

956 Citations

T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

N. J. Yuan;Yu Zheng;Liuhang Zhang;Xing Xie.
IEEE Transactions on Knowledge and Data Engineering (2013)

482 Citations

Discovering Urban Functional ZonesUsing Latent Activity Trajectories

Nicholas Jing Yuan;Yu Zheng;Xing Xie;Yingzi Wang.
IEEE Transactions on Knowledge and Data Engineering (2015)

421 Citations

DRN: A Deep Reinforcement Learning Framework for News Recommendation

Guanjie Zheng;Fuzheng Zhang;Zihan Zheng;Yang Xiang.
the web conference (2018)

390 Citations

Online Discovery of Gathering Patterns over Trajectories

Kai Zheng;Yu Zheng;Nicholas Jing Yuan;Shuo Shang.
IEEE Transactions on Knowledge and Data Engineering (2014)

385 Citations

On discovery of gathering patterns from trajectories

Kai Zheng;Yu Zheng;N. J. Yuan;Shuo Shang.
international conference on data engineering (2013)

259 Citations

Towards efficient search for activity trajectories

Kai Zheng;Shuo Shang;N. J. Yuan;Yi Yang.
international conference on data engineering (2013)

178 Citations

You Are Where You Go: Inferring Demographic Attributes from Location Check-ins

Yuan Zhong;Nicholas Jing Yuan;Wen Zhong;Fuzheng Zhang.
web search and data mining (2015)

170 Citations

Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data

Yingzi Wang;Nicholas Jing Yuan;Defu Lian;Linli Xu.
knowledge discovery and data mining (2015)

168 Citations

We know how you live: exploring the spectrum of urban lifestyles

Nicholas Jing Yuan;Fuzheng Zhang;Defu Lian;Kai Zheng.
conference on online social networks (2013)

105 Citations

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Frequent Co-Authors

External Links

Personal Website for Nicholas Jing Yuan