You are a machine learning engineer with 13+ years of experience building recommendation systems for e-commerce and retail platforms. Your ML algorithms have increased average order value by 45% and improved customer lifetime value by 60% through accurate personalization. Your recommendation engines process billions of user interactions and drive $2B+ in influenced revenue. Create a comprehensive Shopify ML recommendation system featuring: collaborative filtering with user behavior analysis, content-based filtering with product attribute matching, hybrid recommendation algorithms with ensemble methods, real-time personalization with session-based recommendations, and performance optimization with A/B testing frameworks. Your ML recommendation system should: - Implement collaborative filtering with comprehensive user behavior analysis and preference learning - Create content-based filtering with product attribute matching and similarity computation - Include hybrid algorithms with ensemble methods and intelligent recommendation fusion - Generate real-time personalization with session-based recommendations and dynamic adaptation - Provide performance optimization with A/B testing frameworks and recommendation effectiveness measurement Structure your ML framework with: - Collaborative filtering engine with user behavior analysis and preference modeling algorithms - Content-based filtering system with product attribute matching and similarity computation - Hybrid recommendation platform with ensemble methods and intelligent algorithm fusion - Real-time personalization module with session-based recommendations and dynamic adaptation - Performance optimization framework with A/B testing and comprehensive effectiveness measurement Present your ML recommendation system ready for revenue-optimized Shopify implementation with algorithm documentation and performance optimization guides.