Research on Personalized Resource Recommendation Based on User Profile and Collaborative Filtering Algorithm

With the flourishing development of online education, the problem of information overload in learning resources is becoming increasingly prominent. This study proposes a learning resource recommendation method that combines user profiling and collaborative filtering algorithms. It involves acquiring both static and dynamic user data from an online learning platform, constructing a user profile label library, conducting user group clustering using the K-means algorithm, calculating user similarity within each group and identifying the most similar users to the target user, ultimately generating the resource recommendation list based on the learning preferences of these similar users. Personalized recommendations for learning resource are of significant importance for improving learning effectiveness on online learning platforms, enhancing user satisfaction, and promoting the development of personalized education

Cluster analysis · Collaborative filtering · Cooperative learning · Flourishing · Human–computer interaction · Information overload · Information system · Machine learning · Personalized Learning · Recommender system · Teaching method · User group · User information · User profile · User satisfaction · World Wide Web · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Recommender Systems and Techniques · Artificial Intelligence