Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Abstract

In order to meet learners' personalized learning needs, realize learners' personalized development, and solve the problem of learners' information Trek and overload, a development scheme of e-learning resources personalized recommendation system based on Bayesian algorithm is proposed. This paper studies the personalized Association recommendation model integrating association rule mining and Bayesian network, and improves the association rule mining algorithm by combining historical record pruning and Bayesian network verification. In the process of association rule mining, combined with user history, the frequent itemsets in association rules are filtered, and the itemsets below the given threshold are pruned. The pruned item set is input into the Bayesian verification network for personalized verification, and the verification results are sorted and recommended according to the ranking, so as to give priority to the readers who really like the books. The recommendation model solves the problem of weak personalization in the existing recommendation system to a certain extent. Experiments show that Bayesian network can improve the personalization of association recommendation.