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.
