Research on Optimizing the Intelligent Recommendation System of College Courses by Machine Learning Algorithm

This paper proposes a course intelligent recommendation system based on machine learning algorithm to achieve more accurate personalized recommendation. The system uses deep learning model to design an optimized recommendation algorithm by collecting and analyzing multi-dimensional data such as student behavior, course evaluation and personal interests. Collaborative filtering, content-based filtering and deep-learning algorithm based on graph convolutional network (GCN) are introduced to convert the course recommendation problem into a multi-objective optimization problem, and the recommendation precision and the level of personalization are continuously enhanced by training the model. To validate the efficacy of the system, this article conducts extensive model simulation and data analysis. The simulation results show that on a specific data set, the accuracy and recall rate of the recommendation system reach 93% and 88% respectively, which is better than the traditional recommendation algorithm. By comparing with other mainstream algorithms, the recommendation system proposed in this paper performs better in dealing with data sparsity and improving recommendation accuracy, and greatly improves user satisfaction.