Implementing Recommendation Systems at Scale
System Design Interview Roadmap • Advanced Topics Series
What You’ll Master Today
• Hybrid recommendation architectures that balance accuracy with performance • Real-time inference pipelines handling millions of requests per second
• Cold start strategies for new users and items without historical data • A/B testing frameworks for continuous recommendation optimization
The $100 Billion Challenge
When you open Netflix and see “Because you watched Stranger Things,” you’re witnessing one of the most sophisticated prediction engines ever built. That simple row of recommendations drives 80% of viewing time and saves Netflix over $1 billion annually in subscriber retention. Yet most engineers think recommendations are just “show similar items”—missing the intricate dance of real-time data processing, machine learning inference, and user experience optimization happening beneath the surface.
The challenge isn’t building a recommendation engine that works. It’s building one that remains accurate, fast, and cost-effective when serving 230 million users making billions of decisions daily.
The Three-Layer Architecture Pattern
