Interest-determining web browser

Authors:
Khaled Bashir Shaban;Joannes Chan;Raymond Szeto
Affiliations:
Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar;Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Ontario, Canada;Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Ontario, Canada
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

Citing 3
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Abstract

This paper investigates the application of data-mining techniques on a user's browsing history for the purpose of determining the user's interests. More specifically, a system is outlined that attempts to determine certain keywords that a user may or may not be interested in. This is done by first applying a term-frequency/inverse-document frequency filter to extract keywords from webpages in the user's history, after which a Self-Organizing Map (SOM) neural network is utilized to determine if these keywords are of interest to the user. Such a system could enable web-browsers to highlight areas of web pages that may be of higher interest to the user. It is found that while the system is indeed successful in identifying many keywords of user-interest, it also misclassifies many uninteresting words boasting only a 62% accuracy rate.