Please use this identifier to cite or link to this item:
http://hdl.handle.net/11452/22183
Title: | Automatic new topic identification using multiple linear regression |
Authors: | Uludağ Üniversitesi/Mühendislik Mimarlık Fakültesi/Endüstri Mühendisliği Bölümü. Özmutlu, Seda AAH-4480-2021 6603660605 |
Keywords: | Information science & library science Information analysis Topic identification Information retrievals Search engine Regression analysis Regression Search engines Information retrieval Semantic ANOVA Multiple linear regression FMSS Topic identification Minimizing mean flowtime Web search queries Life Identification (control systems) Users ReaL-time methodology Information-seeking Trends Users Automatic programming Data reduction |
Issue Date: | 2006 |
Publisher: | Elsevier Science |
Citation: | Özmutlu, S. (2006). ''Automatic new topic identification using multiple linear regression''. Automatic new topic identification using multiple linear regression, 42(4), 934-950. |
Abstract: | The purpose of this study is to provide automatic new topic identification of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and multi-factor ANOVA on a sample data log from the Excite search engine, we demonstrated that the statistical characteristics of Web search queries, such as time interval, search pattern and position of a query in a user session, are effective on shifting to a new topic. Multiple linear regression is also a successful tool for estimating topic shifts and continuations. The findings of this study provide statistical proof for the relationship between the non-semantic characteristics of Web search queries and the occurrence of topic shifts and continuations. |
URI: | https://www.sciencedirect.com/science/article/pii/S0306457305001378 https://doi.org/10.1016/j.ipm.2005.10.002 http://hdl.handle.net/11452/22183 |
ISSN: | 0306-4573 1873-5371 |
Appears in Collections: | Scopus Web of Science |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.