Analysis and Design of Personalized Learning Resources Recommendation System Based on Collaborative Filtering Algorithm

Personalized recommendation is a powerful means to solve information overload. By analysing the historical data of users, it can recommend the learning resources that users may be interested in. Currently, the commonly used collaborative filtering recommendation algorithm can well support users to discover potential interests and preferences, and provide users with personalized learning resource recommendation. This paper builds a basic algorithm model based on the collaborative filtering algorithm flow. In view of the problems existing in practical application, the popular resource penalty and time attenuation function are introduced to improve the basic algorithm model. The use case model of the system is constructed based on the use case modelling method. On this basis, each subsystem and its function are designed. The application of the research results in this paper can reduce the user’s time loss in choosing learning resources, quickly obtain the required learning resources, and improve the user’s sense of experience on the application system.