Comprehensive Learning Resource Recommendation Engine

Build an advanced recommendation system for educational resources using collaborative filtering and content-based algorithms. Develop a Python-based system that analyzes student learning patterns, course interactions, and resource engagement to suggest personalized learning materials. Implement hybrid recommendation strategies using surprise library, integrate collaborative and content-based filtering, and create a modular system that can adapt to different educational contexts. Include performance metrics, recommendation explanation capabilities, and scalable data processing.