Recommendation Method of Online Teaching Resources in Colleges and Universities Considering Different User Preference Factors

Abstract

In order to improve the recommendation performance of online teaching resources in colleges and universities and the learning efficiency of users, this paper considers user preference factors and studies the recommendation method of online teaching resources in colleges and universities. This paper selects different user preference factors and extracts preference keywords and builds a keyword projection model for different user preference resources based on this. In this paper, Markov is used to construct a keyword probability model, and the TF-IDF algorithm is introduced to refine online teaching resources. According to the calculated closeness of the resources required by the user, this paper realizes the accurate recommendation of resources. The experimental results show that each recommendation performance index of this method has reached a high value. The recommendation accuracy rate of this algorithm is the highest at 82.2%, the recall rate is the highest at 78.7%, the $F$ value is the highest at 83.5%, and the average absolute error is lower than 0.56. And the successful recommendation rate of this method is as high as 97.6%.