Using Page Classification and Association Rule Mining for Personalized Recommendation in Distance Learning

Daling Wang, Yubin Bao, Ge Yu, Guoren Wang

Abstract: With the rapid development of Internet, distance learning applications over Internet become more and more popular. This paper introduces a personalized learning system for web-based distance learning and focus on the web usage mining techniques aimed at personalized recommendation service. First, this paper presents a web page classification method, which uses attribute-oriented induction method according to related domain knowledge shown by a concept hierarchy tree. Second, the paper presents an algorithm of mining association rules with one-support using Freq- Set-Tree. Third, based on their current access patterns, page classes at the home site, page integration from other sites, and the rules discovered in mining, recommendation pages are made and presented for the students.