Adaptive Curriculum Recommendation Engine Database

Build a complex recommendation database system using Python that dynamically generates personalized learning pathways based on student performance, learning styles, and historical academic data. Implement a graph-based database schema using Neo4j that can track student skills, course relationships, and predictive learning progression. Develop advanced query algorithms that can recommend courses with less than 5% recommendation error rate, incorporating machine learning models to continuously refine suggestion accuracy.