What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
His primary areas of study are Information retrieval, Artificial intelligence, Data mining, World Wide Web and Search engine.
His study brings together the fields of Web page and Information retrieval.
His studies deal with areas such as Machine learning, Pattern recognition and Natural language processing as well as Artificial intelligence.
His Data mining research incorporates elements of Structure, Web application, Similarity and Link analysis.
His World Wide Web study integrates concerns from other disciplines, such as Multimedia, Advertising and Ranking.
The Search engine study combines topics in areas such as Online advertising and Market segmentation.
His most cited work include:
- Knowledge graph embedding by translating on hyperplanes (1278 citations)
- Learning to cluster web search results (614 citations)
- Scalable collaborative filtering using cluster-based smoothing (558 citations)
What are the main themes of his work throughout his whole career to date?
Information retrieval, Artificial intelligence, Data mining, Web page and World Wide Web are his primary areas of study.
Information retrieval is represented through his Web search query, Search engine, Web query classification, Query expansion and Ranking research.
The concepts of his Search engine study are interwoven with issues in Online advertising and Relevance.
His Artificial intelligence research integrates issues from Natural language processing, Machine learning and Pattern recognition.
Zheng Chen has included themes like Set, Cluster analysis, Link analysis, Algorithm and Feature selection in his Data mining study.
His research integrates issues of Context, Multimedia and Advertising in his study of World Wide Web.
He most often published in these fields:
- Information retrieval (45.73%)
- Artificial intelligence (33.33%)
- Data mining (26.72%)
What were the highlights of his more recent work (between 2010-2020)?
- Information retrieval (45.73%)
- Artificial intelligence (33.33%)
- Search engine (16.25%)
In recent papers he was focusing on the following fields of study:
Zheng Chen mainly investigates Information retrieval, Artificial intelligence, Search engine, Machine learning and Data mining.
His Information retrieval research includes elements of Web page and Knowledge base.
Many of his studies involve connections with topics such as Natural language processing and Artificial intelligence.
His biological study spans a wide range of topics, including Multi-objective optimization, The Internet, Relevance and Click-through rate.
His Machine learning study incorporates themes from Matrix decomposition, Data set, Relation and Task.
His Data mining research is multidisciplinary, incorporating perspectives in Collaborative filtering, MovieLens, Human–computer interaction, Cluster analysis and Implementation.
Between 2010 and 2020, his most popular works were:
- Knowledge graph embedding by translating on hyperplanes (1278 citations)
- Knowledge Graph and Text Jointly Embedding (291 citations)
- Aligning Knowledge and Text Embeddings by Entity Descriptions (115 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- World Wide Web
Artificial intelligence, Information retrieval, Natural language processing, Machine learning and Embedding are his primary areas of study.
Artificial intelligence and Computer vision are commonly linked in his work.
Information retrieval and Focus are frequently intertwined in his study.
His Natural language processing research is multidisciplinary, incorporating elements of Space, Word-sense disambiguation, Training set and Knowledge base.
The various areas that Zheng Chen examines in his Machine learning study include Reduction, Named-entity recognition, Data mining and Categorization.
Zheng Chen interconnects WordNet and Entity linking in the investigation of issues within Embedding.
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.
