| 个人简介: Hang Li is chiefscientistof the Noah’s Ark Lab at Huawei.He is    also adjunct professor of Peking University and Nanjing University. His    research areas include information retrieval, natural language processing,    statistical machine learning, and data mining. He graduated from Kyoto    University in 1988 and earned his PhD from the University of Tokyo in 1998.    He worked at the NEC lab in Japan during 1991 and 2001, and Microsoft    Research Asia during 2001 and 2012.? He    joined Huawei Technologies in 2012. Hang has more than 100 publications at    top international journals and conferences, including SIGIR, WWW, WSDM, ACL,    EMNLP, ICML, NIPS, and SIGKDD. He and his colleagues’ papers received the    SIGKDD’08 best application paper award, the SIGIR’08 best student paper    award, and the ACL’12 best student paper award. Hang has also been working on    the development of several products. These include Microsoft SQL Server 2005,    Microsoft Office 2007 and Office 2010, Microsoft Live Search 2008, Microsoft    Bing 2009 and Bing 2010. He has also been very active in the research    communities and served or is serving the top conferences and journals. For    example, in 2012, he is track co-chair of the web search track of WWW'12;    senior program committee members or area chairs of WSDM'12, KDD'12, CIKM'12,    ACML'12, AIRS'12; co-chair of KDD'12 summer school, etc; and an editorial    board member on the Journal of the American Society for Information Science,    ACM Transaction on Intelligent Systems and Technology, and the Journal of    Computer Science & Technology.
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    | 报告摘要: I    will start my talk with an introduction to the Noah’s Ark Lab of Huawei and    our vision on big data. I will then introduce our ongoing work on social    information filtering. The ultimate goal of our project is to construct    information assistants for users and help them to easily and quickly access    information. At the first step, we consider leveraging the information on    social media such as Twitter and Weibo for construction of information    assistants. We call such an approach “social information filtering”. I will    explain the basic problems of social information filtering, and discuss the    technical challenges and potential solutions to the challenges. Finally, I    will introduce our ongoing experiments on social information filtering at    Weibo.
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