How Netflix keeps you hooked with personalized show recommendations

How does Netflix keep you hooked with personalized recommendations

Have you ever thought of Netflix as a smart data analytics company?

Obviously not!

People watch Netflix to binge favorite entertaining content curated to their unique watching tastes.

That’s the bottom line — ‘Curated to your unique watching taste.’  Serving personalized content recommendations for every user with a clear intention to not let them drop off from Netflix, ever. (Like all other OTT platforms!)

What makes Netflix win over the competition is its AI-powered decision-making system that accurately delivers shows you would like to see 𝘯𝘰𝘸 𝘢𝘯𝘥 𝘯𝘦𝘹𝘵.

How does the algorithm work?

Todd Yellin, Netflix’s vice president of product innovation explains the magic algorithm with a simple analogy of a three-legged stool.

In a talk with the Wired journalist, Yellin says, “The three legs of this stool would be Netflix members; taggers who understand everything about the content; and our machine learning algorithms that take all of the data and put things together,”

Netflix has over 100 million users globally with multiple user profiles, accounting for almost 250 million active profiles.

And probably no two user profiles will have exactly similar shows listed on their screens! How? Let’s figure it out.

As soon as a user logs in to an account, the algorithm starts tracking specific activities like—what you watch, what you watch after, what you watched before, what you’ve watched recently, and what time of day.

Based on the analysis, AI generates behavioral insights that act as the first leg of the metaphorical stool.

The second leg is Netflix’s dedicated community of content consumers who deeply understand the diverse nuances of every scene in a show and attach it with a relevant tag.

Both these legs combined act as an input to the ML algorithm—the third leg—that identifies patterns in data by finding answers to a series of logical questions like “How much should it matter if a consumer watched something yesterday or a year ago? How about if they watched ten minutes of content and abandoned it or binged through it in two nights?” says Yellin.

With machine learning, those three legs create thousands of ‘taste communities’ around the world that combine people who watch the same kind of things.

So, the show recommendations you see on your screen interface are decided by what taste groups you fall under.

To further curate the content for glocal audiences, ML uses a sub-set of tags in an explicit way to deliver content that resonates with the user’s country, language, and cultural context.

All these layers of complexities are managed by the back-end AI system that performs its magic in a snap to provide users a delightful watching experience every time they choose to turn on Netflix.

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