How YouTube Recommends Videos – Towards Data Science

Recommender Systems are among the most common forms of Machine Learning that users will encounter, whether they're aware of it or not. It powers curated timelines on Facebook and Twitter, and "suggested videos" on YouTube. Previously formulated as a matrix factorization problem that attempts to predict a movie's ratings for a particular user, many are now approaching this problem using deep learning; the intuition is that non-linear combinations of features may yield a better prediction than a traditional matrix factorization approach can. In 2016, Covington, Adams, and Sargin demonstrated the benefits of this approach with "Deep Neural Networks for YouTube Recommendations", making Google one of the first companies to deploy production-level deep neural networks for recommender systems. Given that YouTube is the second most visited website in the United States, with over 400 hours of content uploaded per minute, recommending fresh content poses no straightforward task.