Application of automatic topic identification on Excite Web search engine data logs

The analysis of contextual information in search engine query logs enhances the understanding of Web users’ search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic c...

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Bibliographic Details
Published inInformation processing & management Vol. 41; no. 5; pp. 1243 - 1262
Main Authors Ozmutlu, H. Cenk, Çavdur, Fatih
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.09.2005
Elsevier Science
Elsevier Science Ltd
Subjects
Content analysis
Dempster–Shafer Theory
Evaluation
Exact sciences and technology
Genetic algorithm
Information and communication sciences
Information processing and retrieval
Information retrieval
Information retrieval. Man machine relationship
Information science. Documentation
Online information retrieval
Performance measures
Research process. Evaluation
Sciences and techniques of general use
Search engine
Search engines
Searches
Session identification
Statistics
Studies
Topic identification
Transaction logs
User behavior
User behaviour
World Wide Web
Dempster–Shafer Theory
Topic identification
Genetic algorithm
Session identification
Search engine
Information retrieval
Identification
Dempster-Shafer Theory
Subject
Automatic processing
World wide web
Dempster Shafer theory
Log file
User behavior
Online AccessGet full text
ISSN0306-4573
1873-5371
DOI10.1016/j.ipm.2004.04.018

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Summary:The analysis of contextual information in search engine query logs enhances the understanding of Web users’ search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic changes within a search session is an important branch of search engine user behavior analysis. The purpose of this study is to investigate the properties of a specific topic identification methodology in detail, and to test its validity. The topic identification algorithm’s performance becomes doubtful in various cases. These cases are explored and the reasons underlying the inconsistent performance of automatic topic identification are investigated with statistical analysis and experimental design techniques.
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ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2004.04.018