Demystifying Netflix’s Recommendation Engine

Have you ever wondered how is it possible for Netflix to predict such accurate recommendations every time you open its app or website?

Well, it’s because of its terrific AI recommendation engine. Netflix’s recommendation engine accounts for more than 80% of the TV shows discovered on the platform.

Netflix has an amazing content inventory with more than 7000 movies and shows in its repertoire. So, any person using the services for the first time would be bombarded with awesome video content from everywhere. The result? A completely confused user incapable of deciding what to choose.

But, this scenario never happens. Thanks to its powerful AI-driven recommendation engine, users are in complete control to watch whatever content they want, according to their preferences.

A large platform like Netflix uses its recommendation engine to develop a personalized list of content for each user. Since it’s not possible to offer their entire content catalog at once, it curates it according to the user’s preferences. 

Now let’s explore how Netflix’s recommendation engine actually works

First 90 seconds seal the deal

Researchers at Netflix have found that if a user is not able to find appropriate content within the first 90 seconds after opening Netflix, he or she is likely to switch to another platform. It, therefore utilizes its recommending engine to compel its users to start playing content within those 90 seconds.

“Our personalization efforts, including the global recommendation system, are about helping members find something they will love to watch as soon as they open Netflix,” says Gomez-Uribe, . “Knowing that we have 60 to 90 seconds to help you find something great, it is our goal to develop the most personalized experience as possible, based on your unique preferences and tastes, so we can surface the titles you will enjoy as fast as possible.”

How does Netflix know which movies I want to watch?

Netflix has an amazing sense of predicting accurate content according to user preferences. How does that happen?

It’s all about ‘big data’. Big data literally translates into an extremely large chunk of information or data that is processed by optimized algorithms for shaping better business strategies and outcomes. 

Companies like Netflix collects millions of data points from their users and uses machine learning to analyze and refine the algorithms to rank content according to your preferences. Machine learning helps automate millions of decisions based on user activities. It takes several factors into consideration while predicting videos from its catalog such as:

• Interactions such as your browsing history or your ratings of other tiOther users’ interactions with similar tastes

• Relevant information like genre, categories, titles, the cast of titles

Apart from these, Netflix also takes into consideration some other factors for personalizing your viewing experience like

• The time of the day you usually watch content

• The devices on which you watch them

• The usual time duration of your watching hours

All these user interactions act as data points for the algorithms to deliver a better and personalized performance. 

For example, if you liked watching Stranger Things, Netflix will recommend you to watch Black Mirror based on your watch history, ratings and other interactions.

Even if you have not rated Stranger Things, Netflix’s recommendation engine will analyze your behavioral data (the fact that you binged-watched it over three nights) and recommend you similar content.  

Our AI-powered recommendation engine like Alie collects and analyses both explicit and implicit data to filter a personalized list of recommendations. Integrate Alie to your audio/video streaming platform to deliver personalized content recommendations to your customers.

When does the recommending process start?

The recommending process starts as soon as you open your account on Netflix and create your profile. As soon as you do so, Netflix asks you to choose genres or content categories based on areas that interest you. 

After you choose your preferred genres, Netflix’s recommendation engine receives a kick-start and starts learning from your watch patterns for future predictions. The more time you spend on Netflix, the more accurate your recommendations get.

What’s the secret behind the rows?

Every time you open Netflix, you see a dozen rows filled with immersive content. Every row falls under a specific genre like “Trending now”, “Continue Watching”, “New Releases” and so on.  These rows are specially curated by Netflix for the ease of use of its members. 

Netflix, in addition to bundling content in rows according to categories also ranks individual titles using complex algorithms to deliver a personalized viewing experience.

There are three levels of personalization in each row-

• The categories of the rows

• The titles in each row

• The ranking of each title

When you open the home screen, the rows you see on top are the most recommended ones. Most often, it’s the “Recently Added” section- they are movies or TV shows which are newly released. The reason behind their strategic placement is the fact that you cannot miss them as they are the first things you see on the screen when you open the app or website. 

Similarly, you will find many other rows on your Netflix home page like “Trending Now”, “Binge-worthy TV Programs”, “Because You Watched” and so on. 

The titles in each category are grouped based on similar themes so that users who have watched and liked a content from a particular genre can find more content from that genre. Users can simply swipe left and discover new content with matching themes.

The way in which the titles are placed also plays a great role in offering a unique viewing experience. The titles in each row are placed in order of their ratings. The highest-rated ones start from the left of each row and continue right.

To conclude,

With the help of machine learning and data science, Netflix has managed to build a robust recommender system to keep its viewers hooked on their screens and they have indeed done a great job. The company has confessed to save about $1 billion every year due to its smart recommendation engine.

You too can integrate a powerful AI-powered recommendation system like Alie into your audio/video platform to personalize your viewers’ user experience. 

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