What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Fuzzy logic, Recommender system, Fuzzy set, Collaborative filtering and Data mining.
His Fuzzy logic study improves the overall literature in Artificial intelligence.
Jie Lu combines subjects such as Data modeling and Event with his study of Artificial intelligence.
His studies deal with areas such as Tree structure, Service and Product as well as Recommender system.
His Fuzzy set research incorporates elements of Algorithm and Decision support system.
Jie Lu has included themes like Rank, Customer preference, Component and Fuzzy clustering in his Data mining study.
His most cited work include:
- Recommender system application developments (616 citations)
- Tunable lifetime multiplexing using luminescent nanocrystals (406 citations)
- Transfer learning using computational intelligence (371 citations)
What are the main themes of his work throughout his whole career to date?
Jie Lu spends much of his time researching Artificial intelligence, Fuzzy logic, Data mining, Machine learning and Decision support system.
His research investigates the connection with Artificial intelligence and areas like Domain which intersect with concerns in Knowledge transfer.
His Fuzzy logic study combines topics from a wide range of disciplines, such as Algorithm and Mathematical optimization.
The Data mining study combines topics in areas such as Recommender system, Information retrieval and Collaborative filtering.
Jie Lu interconnects Data modeling and Fuzzy control system in the investigation of issues within Machine learning.
His study in Decision support system is interdisciplinary in nature, drawing from both Intelligent decision support system, Operations research, Knowledge management and Decision analysis.
He most often published in these fields:
- Artificial intelligence (24.63%)
- Fuzzy logic (21.04%)
- Data mining (17.46%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (24.63%)
- Machine learning (15.37%)
- Fuzzy logic (21.04%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Fuzzy logic, Domain and Recommender system are his primary areas of study.
His work deals with themes such as Domain adaptation and Pattern recognition, which intersect with Artificial intelligence.
His Machine learning research is multidisciplinary, incorporating perspectives in Function, Structure and Process.
His Fuzzy logic research integrates issues from Data modeling, Telematics and Data stream mining, Concept drift.
While the research belongs to areas of Domain, he spends his time largely on the problem of Knowledge transfer, intersecting his research to questions surrounding Kernel.
Recommender system is a subfield of Information retrieval that Jie Lu explores.
Between 2017 and 2021, his most popular works were:
- Learning under Concept Drift: A Review (128 citations)
- A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities (45 citations)
- Accumulating regional density dissimilarity for concept drift detection in data streams (39 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Recommender system, Domain, Algorithm and Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
His Machine learning study incorporates themes from Space, Information overload, Process and Service.
Recommender system is a primary field of his research addressed under Information retrieval.
The various areas that Jie Lu examines in his Data mining study include Multi-task learning, Task and Urban computing.
His studies in Fuzzy logic integrate themes in fields like Data modeling and Cluster analysis.
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