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2013-09-11 09:22:19 | 【 【打印】【關閉】

時間:2013年8月23日(周五)上午10:00-12:00
地點:446會議室
摘要
A recommendation system acts in anticipation of the needs of a user and proactively pushes recommendation of the right product to the right people at the right time without requiring a user to issue an explicit query to a search engine. To do this, it explicitly models and learns user preferences while interacting with users, continuously monitors time varying candidate items and user context, and recommend the most relevant items to a specific user when appropriate. In this talk, we will present our work on combining Bayesian statistical machine learning and economics theories to help the software agent decide what to recommend and when to recommend based on Bayesian hierarchical models. Experimental results in various applications (e-commerce, restaurants, etc.) will be presented and discussed.

主講人簡介
Yi Zhang is an Associate Professor in School of Engineering at University of California Santa Cruz, with affiliation in Technology Management Department, Computer Science Department, Applied Math and Statistics Department, and Economics Department. Her research interests are recommendation systems, information retrieval, applied machine learning, natural language processing, and computational economics. She has received various awards, including ACM SIGIR Best Paper Award, National Science Foundation Faculty Career Award, Google Research Award, Microsoft Research Award, and IBM Research Fellowship.

She has serve as program co-chair for IR in CIKM, area chair and PC member for various conferences such as SIGIR, WWW, SIGKDD, and ICML. She is an associate editor for ACM Transaction on Information Systems. She has served as a consultant or a technical adviser for several companies and startups. She received her B.S. from Department of Computer Science & Technology at Tsinghua University and her M.S. and Ph.D. from School of Computer Science at Carnegie Mellon University.

 
 
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