Understanding Netflix Long-Term Satisfaction Recommendation System

Breaking down the recommendation system

Image generated with Ideogram.ai

I am pretty sure that all the readers here know about Netflix.

However, if you didn’t know, Netflix is a video streaming service with a subscription option.

Netflix has become one of the “it” companies that many tech workers are aiming for as their technology development is brave enough to revolutionize streaming services.

Their revolution Is proven by their business model and AI and machine learning implementation.

Speaking of the machine learning implementation, one of the most famous Netflix model implementations is their Recommendation System.

If you open their application, you will first see the screen with the recommended video to watch.

Netflix front page (Source: marketplace.org)

Some people might wonder how the recommendation system works on Netflix and how Netflix can optimize the system, especially with a focus on the subscriber long-term subscriber.

Well, that’s what we will discuss in this article.

We will explore what Netflix has done in the past and how it works with the magic of the recommendation system.

Curious about it? Let’s get into it!

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Netflix Recommendation System

In many recommendation system business models, the aim is to have a better retention customer rate when we compare them without any existing recommendation model.

Netflix's recommendation system is not different. Their high-level target is optimizing their recommender system to maximize customer satisfaction and increase Retention.

However, Netflix's application doesn’t use only one recommendation system. Instead, it uses a combination of different machine learning models and techniques for each part of the Netflix application.

Let’s take a look at the image below.

Deep learning for recommender systems: A Netflix case study (Steck et al. (2021))

Netflix divides each part of its homepage into different problems where different recommendation systems are used to generate recommendations.

By dividing the recommendation system problem into multiple sub-tasks, the Netflix research team can improve the system.

When we talk about recommendations, the main thing that Netflix recommends is a video for users. However, there is so much more than that, as Netflix also produces a recommendation system for: