Patterns of Search: Analyzing and Modeling Web Query Refinement
Department of Computer Science and Engineering
University of Washington
Adaptive Systems and Interaction
Redmond, Washington 98052-6399
Author Email: firstname.lastname@example.org, email@example.com
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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 user modeling, Internet search, information retrieval, server log analysis, learning Bayesian networks from data.
In: Proceedings of the Seventh International Conference on User Modeling, Banff, Canada, June 1999. New York: Springer Wien, pp. 119-128.