How Netflix Uses AI/ML To Keep You Entertained? #26
Part1: Netflix AI/ML Series!
Hello Everyone,
Welcome to the 26th edition of my newsletter ML & AI Cupcakes!
This newsletter is a part of ‘Netflix AI/ML Series’. This series is designed to help you understand how Netflix utilizes AI/ML to solve their business problems.
In this series, we’ll discuss in-depth use-cases of AI/ML with the help of theory and code references.
This newsletter series will be very beneficial to you if you are looking for a career in AI/ML domain with big companies like Netflix.
It’s always a good starting point to know how these companies use AI/ML for their business. Knowing their use-cases will help you in interview preparations.
We all know that Netflix is one of the leading OTT platforms with its reach in 190 countries and availability of diverse range of content.
What makes Netflix remain ahead of its peers even in cutthroat competition?
A vast range of Licensed TV shows and in-house productions.
Extensive use of AI/ML technologies to improve user experience.
Constant strive to remain on the top.
Netflix not only provides a great visual platform, but also solves many customer centric problems to provide best viewing experience. In return, Netflix get to grow their business through increased engagements, improved customer satisfaction and higher retention rates.
In today’s newsletter, we’ll discuss some of the problems where Netflix use AI/ML to provide solutions for a better user experience.
Content Recommendation
Problem
Netflix has a large volume of content available ranging across thriller, comedy, romance, horror, K-drama, political drama, adventure, reality shows etc. It may be overwhelming for the user to decide what content they should watch. No user will click on every movie to check if it aligns with his interests or not.
Solution using AI/ML
Netflix uses AI/ML to save the user from decision fatigue and make recommendations based on their interests.
If you are a first-time user, Netflix asks you some input information life genres you are interested in, profile type (kids, family member or yourself) during the signup process to show recommendations accordingly.
Once you become an already existing user, it keeps refining recommendations based on your watch history. Suppose, if you mainly watch adventure or comedy shows, it’ll recommend similar content for you to watch in future.
The good thing about this recommendation system is that it keeps getting better with time. The more time you spend on the platform, the more recommendation system learns about your preferences (genres, actors etc.) and make better (or more accurate) recommendations.
Different users see different recommendations on their screen personalized as per their watch history. If you like adventure movies and your friend likes romantic movies, both of you will have different recommendations on your screens. You’ll see more of adventure movies on your dashboard and he’ll see more of romantic movies.
Personalized Thumbnails
Problem
A thumbnail is the preview image of a series or a movie. This preview image plays a significant role in influencing users to click on a title. A generic thumbnail may fail in appealing to everyone. Consequently, a user may skip the content that can align with his interest because the thumbnail image doesn’t seem appealing to him.
Solution using AI/ML
Netflix uses AI/ML to help users in discovering the content that aligns with their interest through appealing thumbnails.
Netflix’s algorithms generate personalized thumbnails for the user to tempt him to click on the title. These thumbnails are not randomly generated. These are generated based on user’s watch history (genre, actor etc.).
For example, a particular movie can have two thumbnails say action-themed and romance-themed. If you watch more of action movies, then there are high chances that you’ll be shown the action-themed thumbnail for this movie. If your friend likes romantic movies more, he’ll be shown the romance-themed thumbnail for the same movie. Have you ever noticed this?
This personalized thumbnail strategy will at least make you click on the title and check out for that particular movie.
Whether you like the movie or not, that’s another thing.
Streaming Quality Optimization
Problem
Different users use different devices (mobile, laptop, TV etc.) to watch content on Netflix. Also, they have different internet conditions. There can be problems of bad video quality and buffering due to bad internet connections or a particular device constraint. These problems can ruin user’s viewing experience and can make them switch to other competitive platforms like hotstar, amazon prime etc.
Solution using AI/ML
Netflix uses AI/ML to provide a smooth streaming experience to the users irrespective of their device or internet connection speed.
Their algorithms adjust video quality based on the device, bandwidth and internet speed in real-time. It keeps track of users’ viewing habits like peak watching hours to make sure the nearest regional servers are provided with additional cache during those busy hours.
What other use-cases would you like to add?
In the upcoming newsletters, we’ll discuss what AI/ML algorithms are used by Netflix for the above-mentioned use cases. It’ll be a blend of theory and code examples.
Stay tuned for the same.
Let me know what other related topics you want to me cover.
Writing each newsletter takes a lot of research, time and effort. Just want to make sure it reaches maximum people to help them grow in their AI/ML journey.
It would be great if you could share this newsletter with your network.
See you soon!
-Kavita