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Patterns of Search: Analyzing and Modeling Web Query Refinement

  • Tessa Lau
  • Eric Horvitz
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)

Abstract

We discuss the construction of probabilistic models centering on temporal patterns of query refinement. Our analyses are derived from a large corpus of Web search queries extracted from server logs recorded by a popular Internet search service. We frame the modeling task in terms of pursuing an understanding of probabilistic relationships among temporal patterns of activity, informational goals, and classes of query refinement. We construct Bayesian networks that predict search behavior, with a focus on the progression of queries over time. We review a methodology for abstracting and tagging user queries. After presenting key statistics on query length, query frequency, and informational goals, we describe user models that capture the dynamics of query refinement.

Keywords

Bayesian Network User Model Bayesian Network Model Search Service Query Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Tessa Lau
    • 1
  • Eric Horvitz
    • 2
  1. 1.Department of Computer Science & EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Decision Theory & Adaptive SystemsMicrosoft ResearchRedmondUSA

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