Shi Wan Zhao
RESEARCH STAFF MEMBER
The Information Intelligence and Interaction Department of IBM Research
IBM China Research Laboratory
China
Biography
Shiwan Zhao is a Research Staff Member in the Information Intelligence and Interaction Department of IBM Research - China. He received B.S. and M.S. degrees in computer science from Tsinghua University in 1998 and 2000, respectively. Mr. Zhao subsequently joined the IBM Research - China, where he has been working on pervasive computing, Web 2.0 technologies and recommender systems. Particularly, during the past six years, Mr. Zhao has been focusing on the research of recommender systems, developing innovative recommendation technologies by leveraging user-generated content. His work has appeared in top conferences/journal, like IUI/RecSys/SDM/KDD/TIST. His work in IUI 2008 is one of the first attempts to improve recommendation by incorporating tagging data. Other innovative works include developing a new type of recommender system based on social map with social content data, inferring user preference from social relation data, modeling changing user preference with contextual data, etc. He also filed more than 20 patents. His ongoing projects are to solve real industry problems, including banking and retail industries, by using recommendation technologies. Shiwan Zhao is a Research Staff Member in the Information Intelligence and Interaction Department of IBM Research - China. He received B.S. and M.S. degrees in computer science from Tsinghua University in 1998 and 2000, respectively. Mr. Zhao subsequently joined the IBM Research - China, where he has been working on pervasive computing, Web 2.0 technologies and recommender systems. Particularly, during the past six years, Mr. Zhao has been focusing on the research of recommender systems, developing innovative recommendation technologies by leveraging user-generated content. His work has appeared in top conferences/journal, like IUI/RecSys/SDM/KDD/TIST. His work in IUI 2008 is one of the first attempts to improve recommendation by incorporating tagging data. Other innovative works include developing a new type of recommender system based on social map with social content data, inferring user preference from social relation data, modeling changing user preference with contextual data, etc. He also filed more than 20 patents. His ongoing projects are to solve real industry problems, including banking and retail industries, by using recommendation technologies.
Research Interest
Knowledge Discovery and Data Mining
