Fordham University            The Jesuit University of New York

CIS Department Talk - March 19, 2007

The Department of Computer and Information Science & The Society of Computer Science Present

Speaker:Hui Fong, University of Illinois at Urbana Champagne
Topic:An Axiomatic Approach to Information Retrieval
Date:Monday March 19th, 2:30 pm
Place:John Mulcahy Hall, Room 138


With the explosive growth of online information, we have an urgent need for powerful search engines to help manage and make use of all the information. How effectively search engines, such as Google, help us find useful information directly affects our productivity and quality of life. The effectiveness of any search engine is mainly determined by the underlying information retrieval model. A common deficiency of existing retrieval models is that there is generally no guarantee of the optimality of performance, and heavy parameter tuning is always needed to achieve optimal performance on a particular data set, which not only is labor-intensive, but also provides no guarantee of optimality on future queries.

In this talk, I will present a novel axiomatic framework for information retrieval. This new framework is fundamentally different from all previous retrieval frameworks in that relevance of documents to a query is captured with formalized retrieval constraints defined at the level of terms. I will present specific research results that demonstrate three benefits of such an axiomatic framework: (1) it can predict the performance of a retrieval function analytically without needing empirical experimentation; (2) it serves as a roadmap and provides guidance for developing new effective retrieval functions; and (3) it suggests a novel evaluation methodology that can diagnose strengths and weaknesses of retrieval functions. The axiomatic framework opens up a new promising direction in studying information retrieval models. Using the framework, we have derived several new retrieval functions that are more effective and robust than existing retrieval functions. Moreover, the derived functions can be used in any retrieval applications to improve search accuracy.


Hui Fang is a Ph.D. candidate in Department of Computer Science at University of Illinois at Urbana-Champaign, advised by Prof. ChengXiang Zhai. She obtained her B.E. in Computer Science from Tsinghua University in 2001 and her M.S. in Computer Science from University of Illinois at Urbana-Champaign in 2004. Her primary research interest is information retrieval, with focus on developing effective and robust retrieval models. She is also interested in bioinformatics, data mining and databases. She received the ACM SIGIR 2004 Best Paper Award for her work on information retrieval models.

For more information, contact:
Ms. Diane Roche (718) 817-4480; (

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