Personalised recommendation method of college English online teaching resources based on hidden Markov model

Aiming at the problem of the high comprehensive evaluation index of recall and precision in the traditional personalised recommendation methods of college English online teaching resources, a personalised recommendation method of college English online teaching resources based on hidden Markov model is proposed. First, extract the label features, student learning behaviour features, and time weight features. Then, pre-process the extracted college English online teaching resource data, build a recommendation model based on the pre-processing results, and use a hidden Markov model to process the hidden data to obtain the maximum likelihood estimates the parameter. Finally, input the parameter into the recommendation model to obtain the optimal parameter, that is, the optimal recommendation result. The simulation results show that the comprehensive evaluation index values of precision and recall of the recommended results of the proposed method are within 0.9 and 0.5, which has a good recommendation effect and meets the needs of the development of network teaching.