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CIKM'01 Atlanta, Georgia USA

2

Real Time User Context Modeling
for Information Retrieval Agents

Travis Bauer
Computer Science Department
Lindley Hall, Indiana University
150 S. Woodlawn Avenue
Bloomington, IN 47405, U.S.A.
David B. Leake
Computer Science Department
Lindley Hall, Indiana University
150 S. Woodlawn Avenue
Bloomington, IN 47405, U.S.A.

20 August 2001

leake@cs.indiana.edu

Abstract:

The success of personal information agents depends on their ability to provide task-relevant information. This paper presents WordSieve, a new algorithm that generates context descriptions to guide document indexing and retrieval. WordSieve exploits information about the sequence of accessed documents to identify words which indicate a shift in context. We have tested WordSieve in a personal information agent, Calvin, which monitors a user's document access, generates a representation of the user's task context, indexes the resources consulted, and presents recommendations for other resources that were consulted in similar prior contexts. In initial experiments, WordSieve outperforms term frequency/inverse document frequency at matching documents to hand-coded vector representations of the task contexts in which they were originally consulted, where the task context representations are term vectors representing a specific search task given to the user.



 
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Next: Introduction
Travis Bauer
2002-01-25