What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
His primary areas of investigation include Information retrieval, Data mining, World Wide Web, Social network and Point of interest.
Gao Cong regularly ties together related areas like The Internet in his Information retrieval studies.
The study incorporates disciplines such as Machine learning, Row and Search engine indexing, Artificial intelligence in addition to Data mining.
His Web content study, which is part of a larger body of work in World Wide Web, is frequently linked to Baseline and Focus, bridging the gap between disciplines.
Gao Cong has researched Social network in several fields, including Graphical model and Profiling.
His Point of interest research is multidisciplinary, incorporating elements of Breadth-first search, Recommender system, Graph and Service.
His most cited work include:
- Time-aware point-of-interest recommendation (505 citations)
- Efficient retrieval of the top-k most relevant spatial web objects (467 citations)
- Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (388 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Information retrieval, Artificial intelligence, Theoretical computer science and Machine learning.
His work deals with themes such as Tree, Scalability, Row and Cluster analysis, which intersect with Data mining.
The Information retrieval study which covers World Wide Web that intersects with Point of interest.
His work carried out in the field of Artificial intelligence brings together such families of science as Natural language processing and Pattern recognition.
In his work, Social network is strongly intertwined with Graph, which is a subfield of Theoretical computer science.
His work is dedicated to discovering how Web search query, Query language are connected with Query expansion and other disciplines.
He most often published in these fields:
- Data mining (32.86%)
- Information retrieval (29.58%)
- Artificial intelligence (20.19%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (20.19%)
- Theoretical computer science (12.68%)
- Trajectory (5.16%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Theoretical computer science, Trajectory, Graph and Machine learning are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Sequence modeling and Pattern recognition.
In his study, Human–computer interaction is strongly linked to Recommender system, which falls under the umbrella field of Theoretical computer science.
His work in Graph covers topics such as Graph which are related to areas like Social network, Session and Pairwise comparison.
Gao Cong performs integrative Throughput and Data mining research in his work.
His Data mining study combines topics in areas such as Stability and Dimension.
Between 2018 and 2021, his most popular works were:
- Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation (21 citations)
- HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (20 citations)
- Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
Gao Cong spends much of his time researching Recommender system, Theoretical computer science, Feature learning, Artificial intelligence and Collaborative filtering.
His Recommender system research integrates issues from Session, Graph, Pairwise comparison and Graph.
His research integrates issues of Artificial neural network, Generative model and Statistical model in his study of Feature learning.
Gao Cong combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
His study in Collaborative filtering is interdisciplinary in nature, drawing from both Boosting and Human–computer interaction.
His Metric research focuses on Ranking and how it connects with Recurrent neural network.
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