Development and Utilization of Aesthetic Education Resources Using Collaborative Filtering Model

Abstract

In students’ education, other educational methods cannot take the place of aesthetic education. This study investigates the creation and use of aesthetic education resources and develops a recommendation model for these resources based on the CF algorithm. The conventional CF algorithm is also enhanced by this study. One by one, the solutions are presented with a focus on the traditional CF algorithm’s cold start and data sparseness issues. Additionally, the system’s requirements, business process, and functional structure are examined in the section on system design. The front-end diagnostic test, the background instructional resource management system, and the recommendation of instructional resources are all realized in the realization section. According to the experimental findings, this algorithm’s 95.8% recommendation accuracy is 5.87% higher than the user-based recommendation algorithm and 6.03% higher than the item-based recommendation algorithm. The outcomes demonstrate the accuracy and dependability of this algorithm. It can suggest appropriate resources for aesthetic education, fostering the beneficial and healthy growth of students’ aesthetic education.