What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Data mining, Recommender system, Artificial intelligence, World Wide Web and Machine learning.
His Data mining research is multidisciplinary, relying on both Property, Measure, Data set and Cluster analysis.
His Recommender system research incorporates themes from Topic model, Point of interest, Preference and Mobile device.
His work in the fields of Word, Statistical model and Knowledge extraction overlaps with other areas such as Multiple source.
His World Wide Web research integrates issues from Exploit, Field and Key.
His Machine learning research focuses on Classifier and how it relates to Supercomputer, Support vector machine and Semi-supervised learning.
His most cited work include:
- Understanding of Internal Clustering Validation Measures (477 citations)
- Discovering colocation patterns from spatial data sets: a general approach (353 citations)
- Learning geographical preferences for point-of-interest recommendation (325 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Data mining, Machine learning, Cluster analysis and Information retrieval.
The Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition.
As a part of the same scientific study, Hui Xiong usually deals with the Data mining, concentrating on Graph and frequently concerns with Graph.
Machine learning is closely attributed to Classifier in his work.
Particularly relevant to Recommender system is his body of work in Information retrieval.
His Recommender system study necessitates a more in-depth grasp of World Wide Web.
He most often published in these fields:
- Artificial intelligence (27.27%)
- Data mining (26.65%)
- Machine learning (17.77%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (27.27%)
- Machine learning (17.77%)
- Graph (7.23%)
In recent papers he was focusing on the following fields of study:
Hui Xiong mainly investigates Artificial intelligence, Machine learning, Graph, Data mining and Recommender system.
His work on Deep learning, Embedding and Representation as part of his general Artificial intelligence study is frequently connected to Modal, thereby bridging the divide between different branches of science.
His study in the field of Regularization, Cluster analysis, Artificial neural network and Reinforcement learning is also linked to topics like Clear-air turbulence.
His research in Graph tackles topics such as Graph which are related to areas like Convolution and Algorithm.
His research in Data mining intersects with topics in Fuzzy clustering, Graph neural networks, Traffic flow, Parking guidance and information and Data set.
His Recommender system study is related to the wider topic of Information retrieval.
Between 2019 and 2021, his most popular works were:
- A Comprehensive Survey on Transfer Learning (119 citations)
- EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction (34 citations)
- A survey on knowledge graph-based recommender systems (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Information retrieval, Recommender system, Graph and Data science.
His studies link Machine learning with Artificial intelligence.
The various areas that Hui Xiong examines in his Information retrieval study include Feature, Transportation planning, Mode, Deep learning and Feature learning.
His research on Recommender system also deals with topics like
- User experience design that intertwine with fields like Field and Knowledge graph,
- Information explosion which intersects with area such as Exploit.
His Graph research includes elements of Routing, Fuzzy clustering, Graph, Algorithm and Operations research.
His Data science study incorporates themes from Matching, Vehicle-to-vehicle, Mobile computing and Predictability.
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