What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, World Wide Web, Computer vision, Rendering and Computer graphics.
His Artificial intelligence research incorporates themes from Recommender system, Linear model and Memorization.
The Information needs research he does as part of his general World Wide Web study is frequently linked to other disciplines of science, such as Scale, Social information and Characterization, therefore creating a link between diverse domains of science.
His study in Rendering is interdisciplinary in nature, drawing from both Marching cubes and Graphics.
His work is dedicated to discovering how Marching cubes, Topology are connected with Visualization and other disciplines.
The Computer graphics study combines topics in areas such as Face and Geometric shape.
His most cited work include:
- Wide & Deep Learning for Recommender Systems (1038 citations)
- Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network (876 citations)
- Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles (388 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Information retrieval, Recommender system, World Wide Web and Machine learning.
His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Natural language processing.
His work on Rendering as part of general Computer vision study is frequently connected to Virtual colonoscopy and Motion planning, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His biological study spans a wide range of topics, including Content, Word and Index.
His work in the fields of World Wide Web, such as Social media, intersects with other areas such as Digital library.
His Feature study in the realm of Machine learning interacts with subjects such as Field, Quality and Space.
He most often published in these fields:
- Artificial intelligence (37.29%)
- Information retrieval (25.42%)
- Recommender system (17.80%)
What were the highlights of his more recent work (between 2017-2021)?
- Recommender system (17.80%)
- Artificial intelligence (37.29%)
- Machine learning (16.10%)
In recent papers he was focusing on the following fields of study:
Recommender system, Artificial intelligence, Machine learning, Artificial neural network and Benchmark are his primary areas of study.
His research in Recommender system intersects with topics in Ranking, Embedding, Feature learning and Categorical variable.
Lichan Hong interconnects Natural language understanding, Knowledge transfer, Head, Information retrieval and Transfer of learning in the investigation of issues within Feature learning.
In his research, Table, Overfitting and Feature is intimately related to Theoretical computer science, which falls under the overarching field of Categorical variable.
Specifically, his work in Artificial intelligence is concerned with the study of Deep learning.
In his work, Feature vector is strongly intertwined with Feature, which is a subfield of Deep learning.
Between 2017 and 2021, his most popular works were:
- Fairness in Recommendation Ranking through Pairwise Comparisons (84 citations)
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (73 citations)
- Recommending what video to watch next: a multitask ranking system (61 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Recommender system, Artificial neural network, Machine learning, Artificial intelligence and Ranking.
His Recommender system research incorporates elements of Variety and Table.
His work in Artificial neural network incorporates the disciplines of Matrix decomposition, Multi-task learning and Sample.
His studies deal with areas such as Encoding and Benchmark as well as Machine learning.
His study in Artificial intelligence focuses on Training set in particular.
His work carried out in the field of Ranking brings together such families of science as Pairwise comparison and Data science.
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