How Netflix Knows What You Want to Watch: Matrix Factorization & Architecture

How Netflix Knows What You Want to Watch: Matrix Factorization & Architecture

Have you ever finished a binge-worthy series on Netflix, only for the algorithm to instantly recommend the perfect follow-up show? It feels like magic, but under the hood, it’s one of the most sophisticated Machine Learning systems in the world.

As I dive deeper into Data Science and Machine Learning, I recently studied the architecture behind Netflix's recommendation engine. It’s not just a simple "if/then" script—it requires complex linear algebra and a highly distributed microservices architecture. Here is a technical breakdown of how it actually works.

1. The Math: Matrix Factorization

At the core of many recommendation engines is a technique called Matrix Factorization (often implemented via Singular Value Decomposition or SVD).