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Title:Nonparametric estimation of search query patterns
Author(s):Joo, Soohyung; Wolfram, Dietmar; Song, Suyong
Subject(s):Power Law
non-parametric estimation
kernel regression
query log analysis
Information science--Statistical methods
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.
Issue Date:2013-02
Citation Info:Joo, S., Wolfram, D., & Song, S. (2013). Nonparametric estimation of search query patterns. iConference 2013 Proceedings (pp.919-924). doi:10.9776/13479
Genre:Conference Poster
Publication Status:published or submitted for publication
Peer Reviewed:is peer reviewed
Rights Information:Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2013-02-03

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