University of Illinois Urbana-Champaign

Nonparametric estimation of search query patterns

Joo, Soohyung; Wolfram, Dietmar; Song, Suyong

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479.pdf

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https://hdl.handle.net/2142/42057

Description

Title
Nonparametric estimation of search query patterns
Author(s)
  • Joo, Soohyung
  • Wolfram, Dietmar
  • Song, Suyong
Issue Date
2013-02
Keyword(s)
  • Power Law
  • non-parametric estimation
  • kernel regression
  • query log analysis
  • informetrics
  • Information science--Statistical methods
Date of Ingest
2013-02-03T01:44:36Z
Abstract
In this poster, we adopted nonparametric regression as a method to identify the unique distribution of query log data collected from the Excite search service in May 2001. In Informetrics, parametric modeling has been widely used in tracing term frequency data, such as Zipf’s law, Lotka’s law, or Bradford’s law. However, these traditional parametric methods have had limited application when detecting distributions for large datasets with a nonlinear pattern and a long tail. This study tested kernel regression as an alternative tool to model nonlinearity of term frequency patterns. The results indicated that the kernel regression produced an improved model fit compared to previous parametric approaches in modeling query patterns.
Publisher
iSchools
Type of Resource
text
Genre of Resource
Conference Poster
Language
en
Permalink
http://hdl.handle.net/2142/42057
DOI
https://doi.org/10.9776/13479
Copyright and License Information
Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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