Curriculum Recommendation Engine Using Collaborative Filtering

Build a Python recommendation system using surprise library that suggests personalized learning paths based on student course interaction data. Implement collaborative filtering algorithm that considers student performance, course completion rates, and similarity metrics. Design the system to handle sparse datasets, provide confidence scores for recommendations, and create a modular architecture allowing easy integration with existing learning management systems.