Investigating the Performance of Automatic New Topic Identification Across Multiple Datasets

Özmutlu, H. Cenk and Cavdur, Fatih and Spink, Amanda and Özmutlu, Seda Investigating the Performance of Automatic New Topic Identification Across Multiple Datasets., 2006 . In 69th Annual Meeting of the American Society for Information Science and Technology (ASIST), Austin (US), 3-8 November 2006. [Conference paper]

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English abstract

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.

Item type: Conference paper
Keywords: search engines ; user behavior ; topic identification ; topic differentiation ; subject identification ; subject differentiation
Subjects: I. Information treatment for information services > IB. Content analysis (A and I, class.)
I. Information treatment for information services > IC. Index languages, processes and schemes.
H. Information sources, supports, channels. > HQ. Web pages.
L. Information technology and library technology > LS. Search engines.
Depositing user: Norm Medeiros
Date deposited: 16 Dec 2006
Last modified: 02 Oct 2014 12:05
URI: http://hdl.handle.net/10760/8608

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