Personalised recommendation method of college English online teaching resources based on hidden Markov model
by Qing Tian
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 33, No. 2/3, 2023

Abstract: 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.

Online publication date: Wed, 01-Mar-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com