Alright, alright, hold on to your popcorn because we’re talking about the Netflix recommendation algorithm!
You know that feeling when you’ve got nothing to watch and you’re scrolling through options for hours?
Well, thanks to Netflix’s algorithm, those days are over.
An Introduction to the Netflix Recommendation Algorithm
The incredible algorithms used by Netflix work like pure magic, elevating the platform from a single product to a personalized universe for millions of its users.
This means that Netflix recommendation algorithm has the ability to create hundreds of millions of unique products, one for every member profile. How amazing is that?
In this article, we explore the incredible algorithms that Netflix uses for personalization and how it is one of the best in the personalization space.
Challenges of Personalization at Netflix
Personalization at Netflix is an extremely challenging problem because it requires you to understand people and decide what would they like to watch.
Just to put this into perspective, you must have felt (I’m 101% sure that you have at some point) how tough, at times, it is to decide what to watch. Now imagine deciding the same for 230 million diffrent people.
Let’s introduce another problem. You don’t always watch Netflix alone. Sometimes you watch it with friends and family. It’s not only individual viewing preferences, but rather, Netflix has to consider different group settings and dynamics as well.
Now most importantly, Netflix has over 230 million subscribers worldwide, each with their own unique interests, preferences, and tastes. Plus, they hail from all over the world.
Let me say it – what Netflix does, it’s no less than an engineering marvel.
How Deep is Netflix’s Personalization Game?
Netflix suggests content that matches the interests of its subscribers. Every user interaction on Netflix is tailored based on each users preferences, tastes and previous interactions.
The movies/shows it recommends, their rankings, how they are organized on your screen are all optimized . The thumbnails or short clips that gets played when you hover over a show/movie title is personalized as per your taste and preferences.
Crazy! Isn’t it?
But it doesn’t stop here. Netflix personalizes experiences outside their product as well. Your interactions with emails and other notifications your receive from Netflix are unique. Its algorithms decide what to send, when, to whom, and in what volume.
Simply put, anything Netflix does is personalized and is aimed at increasing customer joy.
Netflix’s 4 Key Approaches to Personalization
Netflix’s platform is always trying to find the perfect show or movie for you to watch.
To make it work, Netflix relies on four key approaches:
- Deep Learning
- Bandits & Reinforcement Learning
1. Deep Learning
Netflix uses deep learning algorithms to analyze vast amounts of user data and identify patterns in viewing behavior.
If you watch action movies or movies featuring a specific actor/director, deep learning algorithms can recognize these subtle patterns.
Identifying these subtle patterns help Netflix recommend relevant and personalized content.
Causality is the study of cause-and-effect relationships between different variables. It’s a crucial component of Netflix’s personalization efforts. It ensures the effectiveness and relevance of Netflix’s recommendations.
Netflix uses causality to understand how users interact with the platform, allowing them to discern user behavior.
Did you watch a movie because Netflix recommended it to you or because you were anyway going to watch it? Netflix uses casualty to find the answers to such questions.
This way they’re able to identify important factors like the type of content users watch and when they’re most inclined to use the platform, allowing them to make accurate recommendations
3. Bandit & Reinforcement Learning
On Netflix’s recommendations feed, the combination of movies, shows, posters and trailers are unique to each user. Netflix uses bandit algorithms to make these choices for you, even when there’s uncertainty about the outcomes.
For example, imagine Netflix has to recommend The DaVinci Code and it has two different posters to use. On one poster, Tom Hanks and Mona Lisa’s paintings are featured, along with the director’s name Ron Howard. In another poster, Tom Hanks is shown holding the Cryptex with the author’s name (Dan Brown) at the bottom.
This way Netflix is able to learn which promotional poster to show to which user subset, so they can maximize viewership.
Netflix uses reinforcement learning to continuously learn from user behavior and make relevant personalized recommendations over time.
Whether you watched a recommended movie or not, the Netflix recommendation algorithm will learn from both positive and negative reinforcements to fine-tune its recommendations in the future.
If you watch a recommended movie and enjoy it, Netflix will recommend similar content in the future, and vice-versa if you didn’t like it.
Each Netflix recommendation is mapped to certain goals. To help you find entertaining movies/shows quickly or to help you find movies/shows you can enjoy over a period of time, say a month?
Objectives are the specific goals Netflix sets for its recommendation system. They help Netflix ensure that it’s recommending content that increases customers’ joy.
“We really want to help members find entertainment that they really want to watch and that they’re going to enjoy watching. This is so we can maximize their satisfaction and the value that they get out of Netflix. And then that in-turn hopefully maximizes their retention so they’ll stay with us longer and we can keep making a better product and more content to then fuel this nice cycle.”
– Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix
How Netflix Personalizes Subscribers’ Viewing Experience?
Netflix divides its personalization efforts into three categories:
- Marketing & Advertisement
Netflix’s homepage provides a personalized viewing experience for each member.
The platform uses fine-grained data, including engagement levels with each individual piece of content consumed, and the mix of content each subscriber is watching. This is used to construct your homepage feed and decide the set of recommendations shown to you.
As a result, the homepage looks different for every user, with suggestions ranked in a way that presents them in the best possible order for you to impulsively pick from.
Netflix understands that different people can like the same content for various reasons. For example, some people like war movies, while others are just fascinated by Brad Pitt, yet both groups would enjoy watching the movie, The Inglorious Basterds.
As a result, every user sees a personalized thumbnail, a different trailer, and different rows of movies or show titles, ranked and organized on the homepage based on the individual user’s preferences, taste, time of the day, and type of device.
All in all, Netflix personalizes your homepage to make it easier for you to find something great to watch no matter what mood you’re in.
The platform processes and analyzes several factors, including your viewing history, searches, and ratings, the type of device you use to stream, and the time you spend watching videos.
To get started, when you create a new Netflix account, you’re forced to select a set of movie titles that you enjoy. This serves as a starting point for the recommendation system. As you continue to watch more contents, the algorithm learns your habits and preferences, and the suggestions become increasingly effective with time.
Netflix’s homepage personalizations allow subscribers to discover new content without expressing their preferences explicitly. On the other hand, Netflix’s search personalization lets users explore new content by expressing their preferences.
Below are the techniques used by Netflix to personalize its search:
Netflix personalizes its search by training its machine learning models that make predictions about which movies or TV shows a user is likely to be interested in.
The data fed into these models includes users’ viewing habits, the content they watch, the duration of the session, and when they stop watching.
Netflix also uses collaborative filtering for personalized searches. The platform analyzes the viewing history of users to identify patterns and similarities between their interests to make personalized recommendations to individual users.
Another technique Netflix uses is content-based filtering. Here it considers attributes like genre, director, actors, and plot of movies or shows to identify patterns and similarities between them. This allows Netflix to recommend movies and TV shows that are similar to ones a user has already watched and enjoyed.
Natural Language Procession (NLP)
NLP allows Netflix to analyze queries and identify the intent behind them. This helps increase the accuracy of recommended search results.
3. Marketing & Advertisement
You know what’s better than personalized recommendations on Netflix? Personalized recommendations sent straight to your inbox or phone.
That’s right! Not only does Netflix recommend amazing content on its platform, but it also sends emails and notifications, delivering billions of messages per year. That’s right, billions!
The Netflix recommendation algorithm is like a matchmaking service. It strives to find the perfect content for you and deliver it to your inbox or phone. It’s a delicate balance between making you happy and not spamming you, but Netflix has got it down to science.
Netflix employs machine learning and statistical techniques like causal models, contextual bandits, and neural networks to find the perfect content match for you. This is how it digs deep to find hidden gems that you never even knew existed.
Netflix doesn’t stop here. It also promotes its platform and original content through programmatic advertising.
Netflix’s budget allocation algorithms behave like a like a marketing guru, deciding what to advertise, to whom, and for how much. It’s like a game of chess, and Netflix always tries to stay one step ahead.
Time to Say Sayonara
We hope you liked this article and encourage you to share your appreciation, criticism or suggestions with us in the comments below.
The entertainment industry has seen the benefits of artificial intelligence in improving user experience, with recommendation systems being a prime example.
So, if you thought the Netflix recommendation algorithm was impressive, you haven’t seen anything yet. There’s a new kid on the block, and its name is Gan.ai.
If you want to personalize videos at scale, Gan.ai is the way to go. With Gan.ai, you can generate millions of personalized videos from a single video recording.
Why spend countless hours creating personalized videos when Gan.ai can do it at scale for you in seconds? Whether it’s for marketing, advertising, or simply to impress your partners and employees, Gan.ai has got you covered.
The algorithms used by Gan.ai are simply magical, just like Netflix, allowing you to create a unique personalized experience for each of your customers, just like Netflix does for its millions of subscribers.
So, if you want to join in on the personalization game, try Gan.ai today and experience the magic for yourself!