Construction of a Personalized Recommendation Service Model for Online Learning Resources

In the digital age, the role of personalized learning resource recommendation system in improving learning experience and educational effect cannot be ignored. Accordingly, this article proposes a personalized recommendation service model for online learning resources to improve the accuracy, efficiency and user attention of the recommendation system. Starting with the data collection and processing of user behavior and the metadata analysis of learning resources, a recommendation algorithm based on collaborative filtering method is designed, and the content recommendation technology is applied to solve the cold start problem. This network architecture adopts micro-service architecture, which ensures the scalability and high concurrent processing ability of the system. The maximum recommendation accuracy of the system reaches 98.3%, the recall rate reaches 99.3%, the maximum response time is 895 milliseconds, and the user satisfaction reaches 8 to 9.9. This article also discusses the current challenges, such as the privacy protection of users, the transparency of recommendation and the real-time performance of the system, and puts forward relevant potential solutions, such as data encryption, enhancing the interpretability of the model and updating the recommendation model in real time. In future work, it can study how to apply deep learning to personal recommendation with higher accuracy.