What is he best known for?
The fields of study he is best known for:
- The Internet
- Artificial intelligence
- World Wide Web
Recommender system, Machine learning, Artificial intelligence, World Wide Web and Social network are his primary areas of study.
His Recommender system study integrates concerns from other disciplines, such as Knowledge base, Vertical search, Search engine and Dialog box.
His work focuses on many connections between Machine learning and other disciplines, such as Information retrieval, that overlap with his field of interest in Probabilistic logic.
His work in Social network tackles topics such as Web application which are related to areas like Social network analysis and Probabilistic matrix factorization.
His Collaborative filtering research integrates issues from Matrix decomposition and Data mining.
Hao Ma has researched Matrix decomposition in several fields, including Theoretical computer science and Approximation algorithm.
His most cited work include:
- Recommender systems with social regularization (1135 citations)
- SoRec: social recommendation using probabilistic matrix factorization (992 citations)
- Learning to recommend with social trust ensemble (662 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Information retrieval, Recommender system, Artificial intelligence, World Wide Web and Machine learning.
Hao Ma has included themes like Ranking, Web page, The Internet and Range in his Information retrieval study.
His study in Recommender system is interdisciplinary in nature, drawing from both Matrix decomposition, Web application, Probabilistic logic and Social network.
His research in the fields of Web modeling, Data Web and Knowledge base overlaps with other disciplines such as Heuristics.
As a part of the same scientific study, Hao Ma usually deals with the Web modeling, concentrating on Web standards and frequently concerns with Web navigation.
His work on Regularization as part of his general Machine learning study is frequently connected to Downstream and Empirical research, thereby bridging the divide between different branches of science.
He most often published in these fields:
- Information retrieval (31.34%)
- Recommender system (29.85%)
- Artificial intelligence (25.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (25.37%)
- Language model (7.46%)
- Machine learning (20.90%)
In recent papers he was focusing on the following fields of study:
Hao Ma spends much of his time researching Artificial intelligence, Language model, Machine learning, Question answering and Self attention.
His research in Artificial intelligence is mostly concerned with Representation.
To a larger extent, Hao Ma studies Natural language processing with the aim of understanding Language model.
His Natural language processing study integrates concerns from other disciplines, such as Word, Leverage and Explicit knowledge.
His research in Machine learning intersects with topics in Text corpus and Automatic summarization.
Hao Ma has researched Self attention in several fields, including Paragraph, Inference, Algorithm and Benchmark.
Between 2018 and 2021, his most popular works were:
- Linformer: Self-Attention with Linear Complexity (81 citations)
- NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization (47 citations)
- Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification (23 citations)
In his most recent research, the most cited papers focused on:
- The Internet
- Artificial intelligence
- World Wide Web
The scientist’s investigation covers issues in Self attention, Algorithm, Linear complexity, Transformer and Theoretical computer science.
His Self attention research is multidisciplinary, relying on both Question answering, Paragraph, Machine learning and Inference.
The Theoretical computer science study combines topics in areas such as Embedding, Sparse matrix, Representation and Bounded function.
In his works, Hao Ma undertakes multidisciplinary study on Factorization and Approximation error.
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