The Netflix Recommender System
Approximately 80+ million members looking to streaming video content in Netflix collection of movies and TV shows
Netflix lies at the intersection of the Internet and storytelling. We are inventing Internet television. Our main product and source of revenue is a subscription service that allows members to stream any video in our collection of movies and TV shows at any time on a wide range of Internet-connected devices. Members stream more than 100 million hours of movies and TV shows per day. The Internet television space is young and competition is ripe, thus innovation is crucial.
A key pillar of our product is the recommender system that helps our members find videos to watch in every session. Our recommender system is not one algorithm, but rather a collection of different algorithms serving different use cases that come together to create the complete Netflix experience.
We seek to grow our business on an enormous scale, that is, becoming a producer and distributor of shows and movies with a fully global reach. We develop and use our recommender system because we believe that it is core to our business for a number of reasons.
Our recommender system helps us win moments of truth: when a member starts a session and we help that member find something engaging within a few seconds, preventing abandonment of our service for an alternative entertainment option.
Personalization enables us to find an audience even for relatively niche videos that would not make sense for broadcast TV models because their audiences would be too small to support significant advertising revenue, or to occupy a broadcast or cable channel time slot. This is very evident in our data, which show that our recommender system spreads viewing across many more videos much more evenly than would an unpersonalized system. We introduce a specific metric: the effective catalog size (ECS) is a metric that describes how spread viewing is across the items in our catalog. If most viewing comes from a single video, it will be close to 1. If all videos generate the same amount of viewing, it is close to the number of videos in the catalog.
Personalization allows us to significantly increase our chances of success when offering recommendations. One metric that gets at this is the take rate - the fraction of recommendations offered resulting in a play. The lift in take-rate that we get from recommendations is substantial. But, most important, when produced and used correctly, recommendations lead to meaningful increases in overall engagement with the product (e.g., streaming hours) and lower subscription cancellations rates. Our subscriber monthly churn is in the low single-digits, and much of that is due to payment failure, rather than an explicit subscriber choice to cancel service. Over years of development of personalization and recommendations, we have reduced churn by several percentage points. Reduction of monthly churn both increases the lifetime value of an existing subscriber, and reduces the number of new subscribers we need to acquire to replace cancelled members. We think the combined effect of personalization and recommendations save us more than $1B per year
The Netflix Recommender System:
Based on the number of capabilities described above, it would be extremely hard to reverse engineer the system. However, anything is possible with the right amount and quality of data. It has already been done (https://netflixprize.com/), even though the scope was much more limited