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How Netflix uses Machine Learning for its Secret Recommendation system

The biggest reason behind Netflix’s success is that it started leveraging Machine Learning before its competitors did. More than 80 per cent of the TV shows people watch on Netflix are discovered through the platform’s recommendation system. That means the majority of what you decide to watch on Netflix is the result of decisions made by a mysterious, black box of an algorithm. Intrigued? Here's how it works.


But that’s not all! Netflix uses machine learning in almost all the facets of its working to provide a seamless experience for users. After all, the data collected by Netflix is huge which includes both the explicit data such as thumbs up or thumbs down for a movie and even implicit data such as data and location where users watch a particular content, the time they watch it for, what device they use, whether they binge-watch it or not, their content choices, online behavior, etc. All this data can be used for machine learning that ultimately improves the bottom line i.e. gets more subscribers for Netflix! So let’s check out the different ways Netflix uses Machine Learning.


1. Content Recommendations


Go to Netflix and check your movie recommendations! Are they the same as your friends? No, your movie recommendations are totally personalized to your tastes and based on what you might want to want.


So if you are a fan of horror movies, you might see more of the Witchy and Ghostly options while your fiend may see cute love story options if they are a fan of rom-coms. But how does Netflix decide this? They use their recommendations system that is based on a machine-learning algorithm that takes into account your past choices in movies, the types of genres you like, and what moves were watched by users that had similar tastes like yours. This movie recommendation algorithm is very important for Netflix, as they have thousands of options of all types and users, are more likely to get confused in choosing what to watch next than actually watching anything. Here the movie recommendation algorithm can provide a clear guideline and help in what to watch. And it’s your choice to follow it or not. If you are in the mood for watching Silence of the Lambs after Pride and Prejudice, then go for it!


Netflix has one of the world’s most sophisticated recommendation systems. Some of the things their recommendation systems consider to suggest a show to you are:


  • Your chosen genres (the genres you choose while setting up the account).

  • The genre of the shows and movies you have watched

  • The actors and directors you have watched.

  • The shows and movies people with a similar taste to yours watch.


There are probably a ton of other factors Netflix uses to determine which shows to recommend. Their goal: to keep you stuck to the screen as long as possible.



2. Auto-Generated Thumbnails


The thumbnails you see for a show or movie aren’t necessarily the ones your best friend sees when they scroll through their homepage.


Surprised? It is true.


Thumbnails can make a lot of difference in whether a user will watch a movie or not. Just imagine you are browsing through Netflix and you see a very interesting thumbnail for a movie or series you’ve never watched. You will definitely click on the thumbnail and check out that movie (whether you light it or not is a different matter!) So the images on the thumbnail can make a big difference for the traffic to a particular movie or series for Netflix. That is why they have personalized auto-generated thumbnail images that are created according to the individual user’s tastes in movies. Netflix uses machine learning to analyze your movie and series choices and understand what sort of thumbnail you are most likely to click. For example, the series like "Stranger Things" can have multiple different thumbnails, a light friendship version or a serious mystery version or a more on the horror side one and you will see the one depending on your tastes. While Stranger Things has all these aspects, the thumbnail according to your preferences will push you in at least checking out these series, and then you can decide if you want to stick or not!




3. Streaming Quality


Buffering can be a huge issue no matter what streaming service you use. People tend to immediately exit the platform after waiting for a few seconds because of buffering. Netflix is well aware of this issue. But how does Netflix ensure the best streaming quality so that there are no glitches even during peak times? It cannot be easy since they have around 200 million subscribers all over the world. Netflix uses machine learning algorithms to predict the viewer patterns and understand when there will be general increases and decreases in viewers of spikes in viewing a certain movie or show. Then they can cache the regional servers that are much closer to the viewers so that there is no log in streaming or loading times even during peak popularity periods.


4. Content Creation


Probably the biggest application of machine learning in Netflix is in content creation. Unlike most production companies, Netflix behaves as a tech enterprise. They don’t create content solely based on the creativity of a few writers or content creators. Instead, they use machine learning algorithms to conduct market research and find which type of content would be the most suited for a particular market segment.


And they use Machine Learning for a supervised quality control algorithm that passes or fails the content such as audio, video, subtitle text, etc. based on the data it was trained on.



As you can see, there are many ways in which Netflix uses Machine Learning. All you see on the site, including your main page and even the recommendations, are all personalized according to your tastes. And this is just the starting! All the facets of your user experience including the seamless video quality, thumbnails you see, the subtitle and audio quality, etc. have some part of machine learning. So the next time you are watching Stranger Things on Netflix, remember that you are not just watching a series but a blending of entertainment and cutting edge technology to provide you this experience.

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