An Efficient Analysis of Web Search Personalization Using Fuzzy Based Approach

Abstract

The paper is intended to explain a process by using the web browsing behaviour of the user to personalize aggregate results from different search engines in accordance with the users' interests. Under the existing taxonomy the various dimensions of Web mining, such as clustering, association rules, navigation, customization, semantic web, recovery of information, text, and image mining, are considered. The role of Fuzzy is highlighted in handling the various types of uncertainty. Classifying web users in a personalised search setup is cumbersome due to the nature of dynamism in user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted within a fuzzy setting. Prior to analysing user behaviour, nature of user interests must be collected. A fuzzy user classification model for a custom web search environment is provided here. A custom browser that is designed for personalization is used to collect user browsing data. Data is flouted by the application of decision trees and fuzzy rules are generated. Here, the search pages are labelled to help user search groups using fuzzy rules.