Research on Integrating Naive Bayes and Collaborative Filtering into an Online-Course Recommendation Model for Universities

Key Points

• The research aims to enhance online course recommendations for universities using machine learning techniques.
• Developed a hybrid recommendation model combining naive bayes and collaborative filtering.
• Analyzed user data to identify preferences and course suitability.
• Evaluated the model's effectiveness through performance metrics.
• The integrated model demonstrated improved accuracy in course recommendations.
• Users reported higher satisfaction with suggested courses compared to traditional methods.

Abstract