Intelligent Recommendation Method of Online Teaching Resources for Business English Collaborative Learning

Due to the large number of online teaching resources, the existing single AI method is difficult to quickly query the required teaching resources, so an intelligent recommendation method of online teaching resources for Business English Collaborative learning is proposed. On the basis of clarifying the classification of business English online teaching resources, the Deterministic Inputs, Noise “And” gate model (TDINA) model is introduced to diagnose students’ cognition and obtain the mastery matrix of student knowledge points. The collaborative filtering algorithm is introduced to develop the online teaching resource recommendation program, and Taste is used as the online teaching resource recommendation engine, thus realizing the intelligent recommendation of online teaching resources. The experimental data shows that the average absolute error and the comprehensive evaluation index value of the proposed method after application are significantly lower than those of the other two comparison methods, with the minimum Mean absolute error of 0.90 and the minimum comprehensive evaluation index of 60%, which fully confirms the better application performance of the proposed method.