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

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