Analysis of a very large web search engine query log
ACM SIGIR Forum
The production of ‘context’ in information seeking research: a metatheoretical view
Information Processing and Management: an International Journal - Special issue on Information Seeking In Context (ISIC)
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Subject categorization of query terms for exploring Web users' search interests
Journal of the American Society for Information Science and Technology
Multitasking information seeking and searching processes
Journal of the American Society for Information Science and Technology
Analysis of large data logs: an application of Poisson sampling on excite web queries
Information Processing and Management: an International Journal
A day in the life of web searching: an exploratory study
Information Processing and Management: an International Journal
Topic modeling for mediated access to very large document collections
Journal of the American Society for Information Science and Technology
Application of automatic topic identification on excite web search engine data logs
Information Processing and Management: an International Journal
A survey on session detection methods in query logs and a proposal for future evaluation
Information Sciences: an International Journal
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One of the most important dimensions of search engine user information seeking behavior and search engine research is content-based behavior, and limited research has focused on content-based behavior of search engine users. The purpose of this study is to present a simulation application on information science, by performing automatic new topic identification in search engine transaction logs using Monte Carlo simulation. Sample data logs from FAST and Excite are used in the study. Findings show that Monte Carlo simulation for new topic identification yields satisfactory results in terms of identifying topic continuations, however the performance measures regarding topic shifts should be improved.