Member-only story
Deep Dive into Netflix’s Recommender System
How Netflix achieved 80% stream time through personalization
Netflix is synonymous to most people in this day and age as the go-to streaming service for movies and tv shows. What most people do not know, however, is that Netflix started out in the late 1990s with a subscription-based model, posting DVDs to people’s homes in the US.
The Netflix Prize
In 2000, Netflix introduced personalised movie recommendations and in 2006, launched Netflix Prize, a machine learning and data mining competition with a $1 million dollar prize money. Back then, Netflix used Cinematch, its proprietary recommender system which had a root mean squared error (RMSE) of 0.9525 and challenged people to beat this benchmark by 10%. The team who could achieve the target or got close to this target after a year would be awarded the prize money.
The winner of the Progress Prize a year later in 2007 used a linear combination of Matrix Factorisation (a.k.a. SVD) and Restricted Boltzmann Machines (RBM), achieving a RMSE of 0.88. Netflix then put those algorithms into production after some adaptations to the source code. What is worth noting is that despite some teams achieving a RMSE of 0.8567 in 2009, the company did not put those algorithms into production due to the…
