With thousands of movies and TV shows at your disposal, how do you decide what to watch? Well, you don’t have to — Netflix delivers a carefully picked selection of media right to your screen. But have you ever wondered how Netflix knows exactly what you like and how it lands on your homepage every time you open the OTT platform?
As of 2021’s first quarter, Netflix claims to have 207.64 million paid subscribers on its platform. But each subscription generally has multiple profiles, meaning the real number of people watching Netflix is far more than that figure. That’s a lot of people and, consequently, a lot of data. So, how does Netflix ensure that all its viewers have a unique content library?
As the company likes to admit, 80% of Netflix’s stream time is credited to its recommendation system, a sophisticated entity that began as a set of analytics tools in the early 2000s to recommend DVDs to users. What it means is that whenever you pick a show or a movie, you are most likely choosing content that the Netflix algorithm decides to show to you. But what is Netflix’s recommender system, and how does it work? Let’s find out.
What Is Netflix’s Recommender System?
The job of a content recommender system is to find patterns based on various parameters and come up with a range of media recommendations for user’s consumption. These parameters include your likes/dislikes, preferences, and overall consumption habits, etc. Also, based on your previous ratings, the recommender system can predict the rating you would assign to any given TV show or movie and recommend it to you if it’s high enough.
Nearly all e-commerce and OTT platforms depend on recommender systems at some level, but what makes Netflix’s software unique is the volume of data and titles at its disposal.
Netflix sifts through all the data, and using its highly complex and proprietary recommender system, offers hyper-personalized content recommendations to its viewers. Did you know that 75% of all the content you consume on Netflix has been recommended by its recommendation system?
So, How Does Netflix Decide Which Movies You Should Watch?
Between 2006 and 2009, Netflix ran a campaign, asking people to improve their then recommender algorithm “Cinematch”. There was a reward of $1 million for whoever could improve its prediction accuracy by 10% (based on how much someone would enjoy a movie given their preferences). The substantial reward alone is a demonstration of how crucial Netflix’s recommendation system is to their business.
The Netflix experience, now, is defined by a number of different algorithms that suggest highly personalized streaming media on your homepage. And according to Netflix, there’s only a window of 1 to 1.5 minutes to display titles to users before they lose interest and log off.
Well, naturally, data drives the system forward — your watching habits, time of day, duration, and so on. There’s also data Netflix gathers from its in-house staff who watch every minute of every title and tag it for genres, theme, etc. (dream job, right?). The user’s data is then mapped, and the streaming media is scored accordingly. These scores keep changing depending on whether the viewer binged it overnight or abandoned it after watching for 5 minutes. But it’s more complicated than that. Let’s see how.
Personalized Video Ranker (PVR)
A Netflix homepage typically shows around 40 rows, with each row having around 70 titles each. These numbers keep changing depending on the device you use to view the content. Nonetheless, all titles are a result of the PVR algorithm, which drives all the genre-specific rows like Comedy, Stand Up, Adventure, Romance, and so on.
This algorithm single-handedly sorts the entire Netflix database based on the user profile.
Because You Watched
The because-you-watched video category recommends you streaming media by putting an anchor on a single show/title you watched. This isn’t as personalized as other recommendations you get on the platform because the titles featured here are based on the consumption of a specific show/title.
Personalization of Content Recommendations For New Users
Although you can always search the entire Netflix catalog for a hit if you can’t find something relevant on your home screen, the recommendations begin as soon as you create a profile. And even while searching, the top results you receive are based on the actions of other viewers on the OTT platform.
When you create a new profile, Netflix asks you to choose a few titles you have perhaps watched earlier and liked. The recommendations you get will be based on these preferences until they are superseded by your watching habits on the platform. If you don’t select anything, you’ll be shown a rather diversified set of popular streaming media.
Again, each title in each row on your homepage is ranked. Netflix’s recommender system also orders each row like “Continue Watching”, “Award-Winning Thrillers”, etc., placing the most strongly recommended rows on the top, and hence providing a more personalized experience.
The Impact of Netflix’s Recommendation System
Netflix has been using the recommender system at some level since the early 2000s, back when it was a movie renting space. As the now evolved OTT platform grew more sophisticated, so did the algorithms behind its recommender system, thanks to the most authentic user data — feedback. And now, it’s saving the media behemoth billions of dollars every year.
Recommendation systems, like the one developed by Argoid, are needed in almost every industry and used by popular platforms like Spotify or Amazon. And now that you know more about Netflix’s famous recommendation system, if you think your business can get better with personalization, it’s high time you started investing in one too. We would be delighted to help you get started.