What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Enhong Chen focuses on Artificial intelligence, Information retrieval, Machine learning, Recommender system and World Wide Web.
His Artificial intelligence research integrates issues from Matrix decomposition, Pattern recognition and Natural language processing.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Noise reduction, Computer vision, Deep learning and Missing data.
Enhong Chen has included themes like Ranking and Mobile search in his Information retrieval study.
His Machine learning research includes themes of Session, Data mining, Data set and Social network.
His World Wide Web research is multidisciplinary, incorporating perspectives in Field, Preference and Mobile computing.
His most cited work include:
- Image Denoising and Inpainting with Deep Neural Networks (962 citations)
- Context-aware query suggestion by mining click-through and session data (443 citations)
- GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (392 citations)
What are the main themes of his work throughout his whole career to date?
Enhong Chen mostly deals with Artificial intelligence, Machine learning, Information retrieval, Data mining and Data science.
His Artificial intelligence research is multidisciplinary, relying on both Pattern recognition, Process and Natural language processing.
His research on Machine learning frequently connects to adjacent areas such as Representation.
As part of his studies on Information retrieval, Enhong Chen frequently links adjacent subjects like Semantics.
He most often published in these fields:
- Artificial intelligence (34.71%)
- Machine learning (19.22%)
- Information retrieval (13.92%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (34.71%)
- Machine learning (19.22%)
- Recommender system (7.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Recommender system, Graph and Pattern recognition.
Enhong Chen has researched Artificial intelligence in several fields, including Computer vision and Natural language processing.
His work on Collaborative filtering and Convolutional neural network as part of his general Machine learning study is frequently connected to Property, thereby bridging the divide between different branches of science.
His research integrates issues of Embedding and Graph in his study of Graph.
His study looks at the intersection of Pattern recognition and topics like Benchmark with Translation and BLEU.
His work deals with themes such as Attention network, Feature learning, Cover and Preference, which intersect with Feature.
Between 2019 and 2021, his most popular works were:
- EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction (34 citations)
- A survey on knowledge graph-based recommender systems (21 citations)
- Voice of Charity: Prospecting the Donation Recurrence & Donor Retention in Crowdfunding (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
Enhong Chen focuses on Artificial intelligence, Machine learning, Artificial neural network, Process and Interpretability.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
His research in Machine learning intersects with topics in Forgetting, Representation, Inference and Word error rate.
His studies deal with areas such as Group method of data handling, Ensemble selection, Leverage and Posterior probability as well as Artificial neural network.
The Process study combines topics in areas such as Student learning, Tracing, Event, Educational technology and Profiling.
The study incorporates disciplines such as Visualization, Recurrent neural network, Hidden Markov model and Knowledge acquisition in addition to Interpretability.
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