Netflix is more than just the biggest streaming service with almost 214 million paid subscribers. It is a highly intelligent service that, for the most part, knows what you want. With over 7000 shows to choose from, it would be beneficial to trust Netflix to make a selection for you. When it comes to series, you might rely much less on your friends' preferences than on Netflix's algorithms. Interested in learning how the recommendation algorithm of the most popular streaming service works based on the information above?
We've conducted the research on your behalf, and now we will elucidate all the specifics of the Netflix recommendation engine. At least, we will disclose the information that Netflix has permitted us to share. Before we delve into the subject, consider the following:
Given that the recommendation engine satisfies its audience and brings in a substantial amount of profits, it must be highly intelligent with minimal room for error. It appears that Netflix genuinely puts forth its best efforts.
- Netflix has 1300 recommendation clusters and 2000 taste groups of viewers;
- Netflix generates $1 billion in revenue thanks to the recommendation algorithms as they help retain most customers;
- Netflix has been refining its algorithms since 2000.
Machine Is Learning, You Are Watching
The Netflix engine is constructed on various mechanisms that compose the horizontal recommendation rows on its interface. It incorporates machine learning techniques (neural networks, reinforcement learning, probabilistic graphical models) and statistics. Additionally, it utilizes mathematics with its causal modeling. Matrix factorization and other methods like ensembles and bandits are specific to recommender systems and are also utilized by Netflix.
Matrix factorization gained widespread recognition due to the Netflix prize, which deserves attention. Netflix is so immersed in recommender systems that it initiated an open competition for the best algorithms to predict users' film ratings solely based on their rating history. In 2009, the team that outperformed Netflix's algorithm by 10% in predicting accuracy received a $1 million prize. The dataset provided for analysis encompassed over 100 million ratings for 17,770 films from almost 500,000 users. An incredibly impressive sample!
Factors That Determines You
Personalization is of utmost importance to Netflix, as it is what drives revenue. Consequently, it employs hundreds of engineers who conduct various analyses based on multiple factors. Users can be segmented based on their behavior, and some of these behavioral factors include:
- The category, year, and genre of the film the user chooses;
- The user's interaction with film ratings and viewing history;
- The duration for which a user watches a show, as well as the time of day when the user is active;
- Similar viewers' tastes;
- The type of device the user possesses.
Personalized Shots for Everyone
When you browse through the Netflix main page filled with recommended shows, you see screenshots of movies or series. Interestingly, these particular screenshots are unique to you! Netflix not only customizes the list of films for you but also the way in which you view it. It randomly selects the most suitable shot and then displays the one with the highest probability. For instance, if you are a fan of Timothee Chalamet, you will see him in the shots of recommended shows for you. If the cast of a particular show includes an actor you prefer more, you will see that actor instead of Timothee on the menu.
Never Stop
The main factor contributing to Netflix's enormous success is likely its ongoing improvement. Netflix initially started as a DVD business in 1997 but gained widespread popularity only a decade later. This growth was influenced by the prevailing trends at that time, the rise of social networks, and the advancement of the internet. Nonetheless, Netflix continues to fine-tune its algorithms and does so each time you visit the service. Thus, Netflix is constantly evolving.
