What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- World Wide Web
- The Internet
Hua-Jun Zeng spends much of his time researching Information retrieval, Data mining, Cluster analysis, Artificial intelligence and Web page.
His Information retrieval study combines topics from a wide range of disciplines, such as World Wide Web and Confidence value.
His research investigates the link between Data mining and topics such as Link analysis that cross with problems in PageRank, Sequential Pattern Mining and Backlink.
His Cluster analysis study integrates concerns from other disciplines, such as Feature vector and Pattern recognition.
The various areas that Hua-Jun Zeng examines in his Artificial intelligence study include Machine learning, Scalability and Natural language processing.
His Web page research is multidisciplinary, incorporating perspectives in Web query classification and Automatic summarization.
His most cited work include:
- Learning to cluster web search results (614 citations)
- Scalable collaborative filtering using cluster-based smoothing (558 citations)
- CubeSVD: a novel approach to personalized Web search (329 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Web page, Artificial intelligence, Data mining and World Wide Web.
His work on Search engine, Web search query, Web query classification and Web search engine as part of his general Information retrieval study is frequently connected to Content, thereby bridging the divide between different branches of science.
In the field of Web search query, his study on Search analytics overlaps with subjects such as Set.
His studies in Web page integrate themes in fields like The Internet and Automatic summarization.
His Artificial intelligence research incorporates themes from Natural language processing, Machine learning and Pattern recognition.
The study incorporates disciplines such as Hyperlink, Categorization, Link analysis, PageRank and Ranking in addition to Data mining.
He most often published in these fields:
- Information retrieval (42.64%)
- Web page (29.46%)
- Artificial intelligence (27.91%)
What were the highlights of his more recent work (between 2006-2018)?
- Information retrieval (42.64%)
- Artificial intelligence (27.91%)
- Web page (29.46%)
In recent papers he was focusing on the following fields of study:
Hua-Jun Zeng mainly focuses on Information retrieval, Artificial intelligence, Web page, Natural language processing and Data mining.
The Information retrieval study combines topics in areas such as Bag-of-words model and Training set.
His work on Question answering as part of general Artificial intelligence study is frequently linked to Scale, therefore connecting diverse disciplines of science.
His work on Static web page and Web navigation as part of general Web page research is often related to Block, thus linking different fields of science.
His Natural language processing research is multidisciplinary, incorporating elements of Identification system, Identification and Cluster analysis.
His Data mining research includes themes of Web log analysis software, Link analysis and Search engine.
Between 2006 and 2018, his most popular works were:
- Demographic prediction based on user's browsing behavior (232 citations)
- Enhancing text clustering by leveraging Wikipedia semantics (186 citations)
- Using Wikipedia knowledge to improve text classification (134 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- World Wide Web
His main research concerns Information retrieval, Natural language processing, Artificial intelligence, Bag-of-words model and Data mining.
His Information retrieval research is multidisciplinary, incorporating perspectives in Representation and Document clustering.
His work in Natural language processing addresses subjects such as Cluster analysis, which are connected to disciplines such as Pattern recognition, Similarity data, Similarity and WordNet.
In the subject of general Artificial intelligence, his work in Paraphrase, Filter, Test set and Natural language is often linked to Matching, thereby combining diverse domains of study.
His Bag-of-words model study combines topics from a wide range of disciplines, such as Similarity measure, The Internet and Leverage.
His biological study spans a wide range of topics, including Web log analysis software, Web application and Web page, Web navigation.
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