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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang.
international acm sigir conference on research and development in information retrieval (2014)
Sequential Recommendation with User Memory Networks
Xu Chen;Hongteng Xu;Yongfeng Zhang;Jiaxi Tang.
web search and data mining (2018)
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang;Xu Chen.
Foundations and Trends in Information Retrieval (2020)
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
Yongfeng Zhang;Qingyao Ai;Xu Chen;W. Bruce Croft.
conference on information and knowledge management (2017)
Towards Conversational Search and Recommendation: System Ask, User Respond
Yongfeng Zhang;Xu Chen;Qingyao Ai;Liu Yang.
conference on information and knowledge management (2018)
Learning heterogeneous knowledge base embeddings for explainable recommendation
Qingyao Ai;Vahid Azizi;Xu Chen;Yongfeng Zhang.
Algorithms (2018)
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Yikun Xian;Zuohui Fu;S. Muthukrishnan;Gerard de Melo.
international acm sigir conference on research and development in information retrieval (2019)
Learning to Rank Features for Recommendation over Multiple Categories
Xu Chen;Zheng Qin;Yongfeng Zhang;Tao Xu.
international acm sigir conference on research and development in information retrieval (2016)
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Liu Yang;Minghui Qiu;Chen Qu;Jiafeng Guo.
international acm sigir conference on research and development in information retrieval (2018)
Personalized Key Frame Recommendation
Xu Chen;Yongfeng Zhang;Qingyao Ai;Hongteng Xu.
international acm sigir conference on research and development in information retrieval (2017)
Tsinghua University
University of Massachusetts Amherst
Rutgers, The State University of New Jersey
Chinese Academy of Sciences
Hong Kong Baptist University
University of Science and Technology of China
Chinese University of Hong Kong, Shenzhen
University of Minnesota
University of California, Santa Cruz
National University of Singapore
Profile was last updated on December 6th, 2021.
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