What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Xiangnan He mainly investigates Artificial intelligence, Machine learning, Recommender system, Collaborative filtering and Embedding.
His work in the fields of Artificial intelligence, such as Deep learning, Artificial neural network and Sequence learning, overlaps with other areas such as Matrix decomposition.
Missing data is closely connected to Data mining in his research, which is encompassed under the umbrella topic of Machine learning.
His Recommender system study is focused on Information retrieval in general.
He combines subjects such as Interpretability, Theoretical computer science, Quantization and Quantization with his study of Collaborative filtering.
His studies in Embedding integrate themes in fields like Tree, Global network, Robustness and Decision tree.
His most cited work include:
- Neural Collaborative Filtering (1578 citations)
- Fast Matrix Factorization for Online Recommendation with Implicit Feedback (507 citations)
- Neural Factorization Machines for Sparse Predictive Analytics (427 citations)
What are the main themes of his work throughout his whole career to date?
Xiangnan He spends much of his time researching Artificial intelligence, Recommender system, Machine learning, Information retrieval and Collaborative filtering.
His research in the fields of Deep learning, Artificial neural network, Embedding and Representation overlaps with other disciplines such as Matrix decomposition.
In his study, Bayesian probability is inextricably linked to Ranking, which falls within the broad field of Recommender system.
His work on Feature is typically connected to Quality, Sampling, Popularity and Generalization as part of general Machine learning study, connecting several disciplines of science.
The Relevance research he does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Raw data, therefore creating a link between diverse domains of science.
His work is dedicated to discovering how Collaborative filtering, Theoretical computer science are connected with Feature learning and Node and other disciplines.
He most often published in these fields:
- Artificial intelligence (45.38%)
- Recommender system (36.55%)
- Machine learning (29.72%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (45.38%)
- Recommender system (36.55%)
- Machine learning (29.72%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Recommender system, Machine learning, Information retrieval and Collaborative filtering.
His Artificial intelligence study typically links adjacent topics like Natural language processing.
He usually deals with Recommender system and limits it to topics linked to Pairwise comparison and Bilinear interpolation.
His Leverage and Interpretability study in the realm of Machine learning interacts with subjects such as Quality, Popularity and Sampling.
Xiangnan He has researched Information retrieval in several fields, including Cover and Social network.
His Collaborative filtering research is multidisciplinary, relying on both Ranking, Embedding, Theoretical computer science and Task analysis.
Between 2019 and 2021, his most popular works were:
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (120 citations)
- Generative Adversarial Active Learning for Unsupervised Outlier Detection (65 citations)
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Xiangnan He mostly deals with Recommender system, Artificial intelligence, Information retrieval, Machine learning and Collaborative filtering.
His Recommender system research integrates issues from Visualization, Key and Natural language.
His work on Deep learning, Reinforcement learning and Attention network as part of general Artificial intelligence research is often related to Granularity, thus linking different fields of science.
His study in the field of Artificial neural network and Overfitting is also linked to topics like Quality and Bipartite graph.
His Collaborative filtering study integrates concerns from other disciplines, such as Embedding, Task analysis and Categorical variable.
His Embedding study incorporates themes from Interpretability and Theoretical computer science.
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