Automated Educational Resource Recommendation Engine

Design a sophisticated Python recommendation system using collaborative filtering and content-based algorithms that automatically curates and suggests educational resources for students. The system should analyze student interaction data, learning history, performance metrics, and resource metadata to generate personalized learning recommendations. Implement a multi-modal approach that considers video content, text resources, interactive modules, and skill-based learning paths with real-time adaptation capabilities.