Using Monte-Carlo simulation for automatic new topic identification of search engine transaction logs

Abstract

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.