Design of Personalized Recommendation System for Teaching Resources Based on Cloud Edge Computing

Abstract

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm.