Interest-determining web browser

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 mis-classifies many uninteresting words boasting only a 62% accuracy rate. © 2010 Springer-Verlag Berlin Heidelberg.