How Does Netflix Recommend Movies? Matrix Factorization

Explore the inner workings of Netflix's movie recommendation system through matrix factorization in this 33-minute video tutorial. Dive into the fundamentals of machine learning and discover how recommendations are generated using the Netflix platform as an example. Learn about matrix factorization techniques for identifying dependencies in user preferences and movie attributes. Understand the benefits of this approach and how to determine the optimal factorization. Examine the error function used in the process and learn how to leverage factors for predicting user ratings. Gain insights into the inference stage, where the system applies learned patterns to make personalized movie suggestions.

What is Machine Learning:
How do recommendations work - Netflix example
How to figure out dependencies - Matrix Factorization
Matrix Factorization Benefits 20:38 How to find the right factorization
Error Function for factorization
How to use the factors to predict ratings - Inference