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Discover the cutting-edge world of Netflix Movies and Series Recommendation Systems and see how it can revolutionize your projects!

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We have all been in a place where after a long day of work, we turn on Netflix to watch our favorite shows. And, thanks to Netflix recommendations, we don't have to worry about what to watch. In this lesson, we will dive deeper into the Netflix movies and series recommendation systems.

The Big Picture 

With over 232.5M paid subscribers worldwide, Netflix has evolved as a successful streaming platform. Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience. This Netflix experience keeps users hooked and away from canceling their subscriptions. Therefore, everything you see on Netflix is a recommendation. 

How It Works

Whenever you turn on Netflix to watch something, the system collects various kinds of data, which is then consumed by multiple algorithms to update recommendations on your home page the very next day. This data includes your location, watch history, ratings, device information, whether you left the show in the middle, etc.

Netflix personalizes your home page and movie artwork that entices you to try it. Netflix uses contextual bandits, rule- and ranking-based approaches to rapidly figure out the optimal personalized artwork or home page for each member and a given context in an online fashion. Netflix search also uses deep learning feed-forward networks to help get results faster if the title you are searching for is available and has a high affinity with their taste profile.

Our Thoughts

Learning about Netflix recommendations can help you understand how recommendation engines work in the real world. This tutorial provides a comprehensive overview of algorithms such as rule-based approaches, contextual bandits, deep learning in the Netflix home page, artwork, and search personalization.

Yes, But 

This lesson is just the tip of the iceberg. There is much more to the Netflix recommendation systems. Stay tuned for future updates where you'll learn about contextual bandits, autoencoders, and computer vision use cases. 

Stay Smart

Dive deeper into the world of recommendation systems with subsequent lessons that explore specific applications like LinkedIn Jobs recommendation systems. Gain insights into the techniques used, including text mining, K-nearest neighbor, clustering, matrix factorization, and neural networks.

Click here to read the full tutorial

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