Personalized Network Learning Recommendation System Algorithm for Deep Learning Mode in Grid Environment

Abstract

With the continuous development of Internet technology, the phenomenon of information overload has appeared in the course learning resources in the network, which increases the difficulty for students to acquire learning resources. To this end, the distributed processing center is built in the grid environment, which enhances the efficiency of data collection and data mining. Combined with the deep learning training mode, the scoring matrix of students and curriculum resources can be effectively obtained, and the scoring matrix is similar. The calculation establishes a recommendation queue for personalized learning recommendation, and finally recommends through the information in the recommendation queue, which effectively improves the accuracy and efficiency of personalized network learning recommendation.