Automatic new topic identification using multiple linear regression

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...

Full description

Saved in:
Bibliographic Details
Published in:Information processing & management 2006-07, Vol.42 (4), p.934-950
Main Author: Ozmutlu, Seda
Format: Article
Language:English
Subjects:
ANOVA
Evaluation
Exact sciences and technology
Information and communication sciences
Information management
Information processing
Information processing and retrieval
Information retrieval
Information retrieval. Man machine relationship
Information science. Documentation
Online information retrieval
Performance measures
Queries
Regression
Regression analysis
Research process. Evaluation
Sciences and techniques of general use
Search engine
Search engines
Studies
Topic identification
Transaction logs
Use statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2005.10.002