Longbing Cao

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Machine learning
• Statistics

His primary areas of investigation include Data science, Artificial intelligence, Data mining, Knowledge extraction and Machine learning. His Data science research integrates issues from Domain, Field, Information technology and Knowledge management. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Contrast and Pattern recognition.

His Data mining research is multidisciplinary, relying on both Information extraction, Algorithm design and Pruning. His research in Knowledge extraction intersects with topics in Association rule learning, Autonomous agent, Decision support system and Domain knowledge. His work carried out in the field of Machine learning brings together such families of science as Fuzzy set operations and Fuzzy classification.

His most cited work include:

• Training deep neural networks on imbalanced data sets (165 citations)
• Personalized recommendation via cross-domain triadic factorization (162 citations)
• USpan: an efficient algorithm for mining high utility sequential patterns (156 citations)

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

His primary areas of study are Data mining, Artificial intelligence, Data science, Pattern recognition and Machine learning. His studies in Data mining integrate themes in fields like Multi-agent system and Cluster analysis. His research on Artificial intelligence frequently connects to adjacent areas such as Categorical variable.

His Data science research includes themes of Domain, Actionable knowledge, Knowledge extraction and Knowledge management. Longbing Cao studies Pattern recognition, focusing on Feature selection in particular. Longbing Cao is studying Recommender system, which is a component of Machine learning.

He most often published in these fields:

• Data mining (30.00%)
• Artificial intelligence (29.30%)
• Data science (23.72%)

What were the highlights of his more recent work (between 2017-2021)?

• Data science (23.72%)
• Artificial intelligence (29.30%)
• Recommender system (8.14%)

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

Data science, Artificial intelligence, Recommender system, Anomaly detection and Theoretical computer science are his primary areas of study. The concepts of his Data science study are interwoven with issues in Order, Key and Big data. His Artificial intelligence research includes themes of Machine learning, Categorical variable and Pattern recognition.

His Recommender system research incorporates elements of Intelligent decision support system, Preference and Categorization. Longbing Cao undertakes interdisciplinary study in the fields of Focus and Data mining through his works. His Data mining study integrates concerns from other disciplines, such as Principle of maximum entropy and Hash function.

Between 2017 and 2021, his most popular works were:

• GeoMF++: Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization (50 citations)
• Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (49 citations)
• Attention-based transactional context embedding for next-item recommendation (46 citations)

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

• Artificial intelligence
• Machine learning
• Statistics

Longbing Cao focuses on Recommender system, Artificial intelligence, Data science, Data mining and Key. To a larger extent, he studies Machine learning with the aim of understanding Recommender system. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition.

His Pattern recognition research integrates issues from Leverage and Cluster analysis. His studies deal with areas such as Cold start recommendation, Relation, Categorization and Interpretability as well as Data science. His work carried out in the field of Data mining brings together such families of science as Bitmap, Data structure and Computer data storage.