IMS-Microsoft Research Workshop: Foundations of Data Science – Opening Remarks and Morning Session I

OPENING REMARKS Richard A. Davis

Susan Dumais Microsoft Research Session Chair Intro: Information Retrieval and Social Media Session

Cheng Zhai University of Illinois, Urbana Champaign Information Retrieval as Cooperative Game Playing: A Bayesian Decision-Theoretic Framework for Optimizing Intelligent Search Systems

Search engines play an important role in helping people manage and exploit big text data. The current-generation search engines are fundamentally limited by their narrow definition of the task of information retrieval (IR) as to rank a collection of documents in response to a query. Such a narrow definition does not model accurately the actual retrieval task in a real IR application, where users tend to be engaged in an interactive process with multipe queries, and optimizing the overall performance of an IR system on an entire search session is far more important than its performance on an individual query. In this talk, I will present a new game-theoretic formulation of the IR problem where IR would be regarded as a process of a search engine and a user playing a cooperative game, with a shared goal of satisfying the user’s information need while minimizing the user’s effort and the resource overhead on the retrieval system. I will present a Bayesian decision-theoretic framework for optimizing the actions of such an intelligent interactive search system, where a formal user model would play a central role to tie statistical language modeling, machine learning, and intelligent information retrieval in a unified decision-theoretic framework. The new framework offers two important benefits. First, it naturally suggests optimization of the overall utility of an interactive retrieval system over a whole search session, thus breaking the limitation of the traditional formulation that optimizes ranking of documents for a single query. Second, it models the interactions between users and a search engine, and thus can optimize the collaboration of a search engine and its users, maximizing the “combined intelligence” of a system and users. I will discuss how the new framework not only covers multiple emerging directions in current IR research as special cases, but also opens up many interesting new interdisciplinary research directions in the intersections of information retrieval, machine learning, statistical decision theory, and computational user modeling.

发言人详细信息

Howard Levene Professor and Chair of Statistics President-elect of IMS

ChengXiang Zhai is an Assistant Professor of Computer Science at the University of Illinois at Urbana-Champaign. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and, later, a Senior Research Scientist from 1997 to 2000. His research interests broadly include text information management, natural language processing, machine learning, and bioinformatics. His early work touches many areas of information retrieval including ad hoc retrieval, phrase indexing, adaptive and collaborative information filtering, OCR document retrieval, foreign language IR, and non-textual information retrieval. His recent work is centered on developing formal retrieval frameworks and applying statistical language models to text information management, especially in directions such as personalized search and theme-based text mining. He also works on biological literature mining and DNA/Protein sequence motif analysis. He is the inventor of four US patents on adaptive information filtering. He was the IR program co-chair of ACM CIKM 2004 and has served on the program committee of many major conferences on information retrieval, natural language processing, data mining, and machine learning. He received the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE) and the best paper award of ACM SIGIR 2004. He received an NSF CAREER award in 2004.

日期：
演讲者：
Richard A. Davis and Cheng Zhai
所属机构：
University of Illinois, Urbana Champaign