Extracting User’s Interests from Web Log Data for Implementing Adaptive Education System

Abstract

As World Wide Web is a repository of web pagesand links, it provides not only useful information forthe Internet users but also becomes delivery platformfor searching and surfing day by day. Webpersonalization is the process of customizing a Website to the needs of each specific user or set of users,taking advantage of the knowledge required throughthe analysis of the user’s navigation behavior.Integration usage data with user profile dataenhances the personalization process. In this paper,the adaptive educational system is developed toextract user’s interests from web log data andimplemented the recommender system to suggest thenext links for studying next. The SPADE (SequentialPattern Discovery using Equivalence classes) is usedin finding semantic association rules to overcome theburden of repeated database scans while calculatingthe support of the candidates and DynamicLCS(Longest Common Subsequence) is applied inmapping with users’ current session and associationrules which are generated from the SPADE algorithm.In the proposed system, the teacher and the contentdeveloper are performed their tasks to become themost accurate information for the bestrecommendations by using domain ontology. Themain objective of this proposed system is to analyzethe student’s behavioral patterns to recommend thenew links that best match the individual user’s preferences