Nonparametric estimation of search query patterns

Joo, Soohyung; Wolfram, Dietmar; Song, Suyong

Permalink

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

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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