Research on Course Recommendation Technology of Computer Distance English Education System Based on Collaborative Filtering Algorithm with Slop One

This study primarily explores the application of the Slop One-based collaborative filtering algorithm in the course recommendation technology of computer-based remote English education systems. Firstly, we conducted an in-depth research and understanding of the Slop One algorithm, and then applied it to the computer-based remote English education system to enhance the accuracy and efficiency of course recommendations. We collected a large amount of user behavior data, including browsing history, click records, learning progress, etc. Through the Slop One algorithm, we analyzed and processed this data to generate user interest models. Then, based on these interest models, we recommended courses that users might be interested in. Compared with traditional recommendation algorithms, our algorithm can better meet users' personalized needs, improve their learning satisfaction and effectiveness. In addition, we evaluated the performance of the algorithm, and the results showed that our algorithm can maintain good performance when dealing with large-scale data, which is very important for computer-based remote English education systems. In summary, the Slop One-based collaborative filtering algorithm provides an effective course recommendation method for computer-based remote English education systems, which contributes to improving the teaching quality and user experience of the system.