Adaptive Personalized Learning Path Generator

Develop a sophisticated Python recommendation engine using collaborative filtering and graph-based algorithms that dynamically generates personalized learning paths for students. Implement a multi-factor scoring system that considers individual learning styles, historical performance, content difficulty, and skill adjacency. The system should utilize NetworkX for skill graph modeling and include a recommendation confidence scoring mechanism that can be integrated into existing Learning Management Systems.