Research on Personalized Recommendation Algorithms in Modern Distance Education Systems

With the rapid development of information technology, modern distance education systems have become an important means of accessing educational resources. However, given the vast amount of learning content and diverse user needs, providing personalized recommendations for learners has become a critical issue. This paper focuses on the study of personalized recommendation algorithms in modern distance education systems, analyzing common algorithms such as collaborative filtering, content-based recommendations, and hybrid approaches, and designing improvements based on the characteristics of distance education. Through experimental validation, the proposed personalized recommendation algorithm significantly improves recommendation accuracy and system performance, enhancing the learning experience for users. Finally, this paper evaluates the algorithm's application through real-world case studies and provides insights into future research directions.