Machine learning-driven intelligent recommendation system for English teaching content

The purpose of this paper is to develop an intelligent English teaching content recommendation system based on ML (Machine Learning), so as to solve the problems existing in the current English teaching content recommendation, such as low efficiency and inability to meet the individual needs of students. In order to achieve this goal, this paper designs the overall architecture of the system, including data acquisition layer, data processing layer, model training layer and recommendation service layer, and preprocesses the data to extract effective features. In the aspect of algorithm model selection, this paper comprehensively compares various ML algorithms, and finally chooses a hybrid algorithm model that combines the advantages of CF (Collaborative Filtering) and DL (Deep Learning). The experiment uses rich data sets to train and verify the model, and through continuous optimization and testing, the performance data of the system on the test set are obtained. The experimental results show that the intelligent recommendation system has high recommendation accuracy and recall rate, and can accurately recommend relevant teaching content according to students' learning history and preferences. At the same time, the real-time and stability of the system have also been verified, which meets the needs of practical application.