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

Abstract

As World Wide Web is a repository of web pages and links, it provides not only useful information for the Internet users but also becomes delivery platform for searching and surfing day by day. Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge required through the analysis of the user’s navigation behavior. Integration usage data with user profile data enhances the personalization process. In this paper, the adaptive educational system is developed to extract user’s interests from web log data and implemented the recommender system to suggest the next links for studying next. The SPADE (Sequential Pattern Discovery using Equivalence classes) is used in finding semantic association rules to overcome the burden of repeated database scans while calculating the support of the candidates and Dynamic LCS(Longest Common Subsequence) is applied in mapping with users’ current session and association rules which are generated from the SPADE algorithm. In the proposed system, the teacher and the content developer are performed their tasks to become the most accurate information for the best recommendations by using domain ontology. The main objective of this proposed system is to analyze the student’s behavioral patterns to recommend the new links that best match the individual user’s preferences.