What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Yongfeng Zhang mainly focuses on Recommender system, Artificial intelligence, Collaborative filtering, Information retrieval and Machine learning.
The Recommender system study combines topics in areas such as Learning to rank, Purchasing, Human–computer interaction and Taxonomy.
His Artificial intelligence research incorporates themes from Ranking and Function.
As a part of the same scientific study, Yongfeng Zhang usually deals with the Collaborative filtering, concentrating on Sentiment analysis and frequently concerns with Phrase.
His Information retrieval research includes elements of Feature learning, Markov chain and Personalization.
In general Machine learning, his work in Leverage and Pruning is often linked to Product and User modeling linking many areas of study.
His most cited work include:
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (412 citations)
- Sequential Recommendation with User Memory Networks (187 citations)
- Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (151 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Recommender system, Artificial intelligence, Information retrieval, Machine learning and Collaborative filtering.
His work often combines Recommender system and Product studies.
His Natural language processing research extends to the thematically linked field of Artificial intelligence.
His Information retrieval research is multidisciplinary, relying on both Ranking, Embedding and Feature learning.
His Sentiment analysis study in the realm of Machine learning interacts with subjects such as Path.
Yongfeng Zhang studies Web page, a branch of World Wide Web.
He most often published in these fields:
- Recommender system (27.63%)
- Artificial intelligence (17.76%)
- Information retrieval (12.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Recommender system (27.63%)
- Artificial intelligence (17.76%)
- Machine learning (10.53%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Machine learning, Information retrieval and Vacancy defect.
His Recommender system study focuses on Collaborative filtering in particular.
Yongfeng Zhang combines subjects such as Microeconomics, Decision theory and Game theory with his study of Collaborative filtering.
His Machine learning research is multidisciplinary, incorporating perspectives in Variety, Space and Generative grammar.
The study incorporates disciplines such as Ranking and E-commerce in addition to Information retrieval.
His research investigates the connection between Vacancy defect and topics such as Density functional theory that intersect with issues in Ostwald ripening.
Between 2019 and 2021, his most popular works were:
- Explainable Recommendation: A Survey and New Perspectives (110 citations)
- Adaptive integral sliding mode control with payload sway reduction for 4-DOF tower crane systems (18 citations)
- Fairness-Aware Explainable Recommendation over Knowledge Graphs (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Yongfeng Zhang spends much of his time researching Recommender system, Artificial intelligence, Knowledge graph, Machine learning and Collaborative filtering.
Yongfeng Zhang interconnects Quality and Operations research in the investigation of issues within Recommender system.
His Artificial intelligence study typically links adjacent topics like Pipeline.
His study with Knowledge graph involves better knowledge in Information retrieval.
His study in the field of Information seeking is also linked to topics like Construct.
He has included themes like Ranking, Training set and Heuristic in his Collaborative filtering study.
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