What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
His main research concerns Information retrieval, Artificial intelligence, World Wide Web, Machine learning and Sentiment analysis.
His work in the fields of Information retrieval, such as Search engine, intersects with other areas such as Computer user satisfaction.
Many of his studies involve connections with topics such as Social media and Artificial intelligence.
His work on Recommender system as part of general Machine learning research is frequently linked to Sparse matrix and Matrix decomposition, thereby connecting diverse disciplines of science.
He has researched Recommender system in several fields, including Space, Feature, Purchasing and Interpretability.
His study looks at the relationship between Sentiment analysis and fields such as Phrase, as well as how they intersect with chemical problems.
His most cited work include:
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (412 citations)
- Automatic online news issue construction in web environment (101 citations)
- Rating-boosted latent topics: understanding users and items with ratings and reviews (90 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Information retrieval, Artificial intelligence, Search engine, World Wide Web and Machine learning.
The concepts of his Information retrieval study are interwoven with issues in Ranking and Web page.
His work carried out in the field of Artificial intelligence brings together such families of science as Context, Recommender system and Natural language processing.
His study in the field of Collaborative filtering is also linked to topics like Matrix decomposition.
His research investigates the connection between Search engine and topics such as Learning to rank that intersect with issues in Ranking SVM.
His work in the fields of The Internet and Social network overlaps with other areas such as Focus.
He most often published in these fields:
- Information retrieval (50.58%)
- Artificial intelligence (29.65%)
- Search engine (25.58%)
What were the highlights of his more recent work (between 2018-2021)?
- Information retrieval (50.58%)
- Artificial intelligence (29.65%)
- Relevance (13.95%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Relevance, Recommender system and Ranking.
His work in the fields of Information retrieval, such as Search engine and Click model, overlaps with other areas such as Logging.
The various areas that Shaoping Ma examines in his Artificial intelligence study include Machine learning and Natural language processing.
Image is closely connected to Context in his research, which is encompassed under the umbrella topic of Machine learning.
His Relevance study integrates concerns from other disciplines, such as Data mining and Web mining.
His study in the fields of Collaborative filtering under the domain of Recommender system overlaps with other disciplines such as Sequence.
Between 2018 and 2021, his most popular works were:
- Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (42 citations)
- Human Behavior Inspired Machine Reading Comprehension (14 citations)
- Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- World Wide Web
His primary areas of study are Information retrieval, Recommender system, Relevance, Artificial intelligence and Session.
Shaoping Ma usually deals with Information retrieval and limits it to topics linked to Ranking and Ranking.
The study incorporates disciplines such as Embedding, Representation, Preference and Flexibility in addition to Recommender system.
His work deals with themes such as Crowdsourcing, Heuristic, Range and Search engine, which intersect with Relevance.
While the research belongs to areas of Artificial intelligence, Shaoping Ma spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Sampling.
The Session study combines topics in areas such as Exploratory search and Click-through rate.
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