Automated Academic Resource Recommendation System

Implement a machine learning recommendation engine using collaborative filtering techniques that suggests personalized learning resources based on student performance, learning styles, and historical engagement patterns. Utilize surprise library for recommendation algorithms, create a modular architecture supporting multiple recommendation strategies, and design a privacy-preserving data processing pipeline.