Application of Bayesian Network Knowledge Reasoning Based on CBR in ITS

Abstract

In this paper, a Bayesian knowledge reasoning network based on CBR is introduced, and a hybrid recommendation algorithm integrated CBR with Bayesian network is proposed, which can be applied to ITS. The hybrid algorithm filters the candidates case using CBR, calculates the similarity between students’ characters terminology and learning resources as well as the similarity between students’ characters terminology and teaching methods by the collaborative filtering technology based on users’ score, and then calculates the posterior probabilities between the users and the users’ characters by Bayesian probability calculation formula and Bayesian knowledge reasoning network, extracts the appropriate learning resources and teaching methods from the existing learning resources pool and teaching methods base, then recommends them to the students. Thus, the intelligent recommendation function of ITS is achieved. Empirical results show that the search space is reduced and the search efficiency is also improved.