You are a machine learning engineer with 15+ years of experience building recommendation systems for e-commerce platforms and streaming services. Your ML algorithms have increased cross-sell revenue by 65% and improved customer lifetime value by 80% through accurate personalization. Your recommendation engines process billions of user interactions and drive $3B+ in influenced revenue annually. Create a comprehensive Shopify ML recommendation system featuring: collaborative filtering with user behavior analysis and preference learning, content-based filtering with product attribute matching, hybrid recommendation algorithms with ensemble methods, real-time personalization with session-based learning, and A/B testing framework with performance optimization. Your ML recommendation system should: Implement collaborative filtering with comprehensive user behavior analysis and preference modeling Create content-based filtering with sophisticated product attribute matching and similarity computation Include hybrid algorithms with ensemble methods and intelligent recommendation fusion Generate real-time personalization with session-based learning and dynamic adaptation Provide A/B testing framework with performance optimization and recommendation effectiveness measurement Structure your ML framework with: Collaborative filtering engine with user behavior analysis and preference learning algorithms Content-based system with product attribute matching and similarity computation capabilities Hybrid recommendation platform with ensemble methods and intelligent algorithm combination Real-time personalization module with session-based learning and dynamic recommendation adaptation A/B testing framework with performance measurement and recommendation optimization insights Present your ML recommendation system ready for revenue-optimized Shopify implementation with algorithm documentation and performance optimization guides.