Netflix Recommendations System: How does Netflix work?

Netflix Recommendations System: How does Netflix work?

How Netflix works? How Netflix get to know that how and which movies should recommend to you and How Netflix creates a Personalized homepage for you? 

How movies are selected in Top Trending?  If you read this blog completely, only then you will understand how the Netflix features work. 

So in this blog, I will tell you How 5 features of Netflix work and how machine learning is used in it. As you all know Netflix is a streaming service and it has more than 67million active users. 

Now Netflix has so many users and for each of those users according to rank It setup a Personalised Home page for them. This home page designed on the basis of Ranking. So how is this done? Let me explain how Netflix’s Home page is designed. 

In Netflix there are 5 features names are as follows:-

Ranking and Layout 

Similarities and Promotions

Evidence and search

Thumbnail Automation

Top 10 Trending Features

So Let’s start with the first Feature of Netflix Ranking and Layout. Let me explain to you the first Feature with an Example Suppose a user who likes watching comedy movies rather than Romantic movies and thriller movies. So he has seen all the comedy movies mostly and rarely seen romantic and thriller movies. 

So here the work of machine language becomes that once Netflix finds out that your interest is embedded in which movies. So here the recommendations of comedy movies will be rooted out and Romantic and Thriller Movies will be reduced. 

Through this ranking, recommendations are given and a layout is prepared for the user, And this is the Homepage of Netflix. So this is how Netflix deeds which users to recommend which movies.

Netflix always tries to give a better experience or most Valuable content to its users and that could be based on User’s past behavior and Watch history.

Let us now talk about the next feature which is Similarities and Promotions, Once Netflix finds out about your interest, it would find the similar content across the platform, after which you will be recommended. Let me tell you for the example  Whatever recommendations on your screen in Netflix  ” because you all see these movies instead would you like to watch this one also “.

In the first point Netflix came to know what your interest is and the second time it will use that data to find similar content and will recommend you after that. 

Now next I’m going to tell you about the next feature of Netflix which is Evidence or search, What I mentioned earlier 2 features is content-based recommendations in which the user’s interest is seen according to the User’s interest user gets the content recommended by Netflix.

So what now I am going to talk about here is Collaborative Filtering. These recommendations are in a way different from the content-based recommendations because some groups are made in it. Like here are 3 users to whom Netflix recommended some movies now user no.1 and User no.2  but User 1 and User 3 have there Interested on the same topic instead of the user no. 2.

So what will happens in Collaborative filtering Now the user no.1 and the user no.3 will move to Group A and the User no. 2 moves to group B. So here first the pattern of the users is followed, after which the groups of users of similar types are made after this Netflix recommends the Movies to the Users of their Interest.

Now let’s move to next topic which Is Thumbnail automation So, let me tell all of you that whatever happens, did you notice that whenever you watch any web series and then you click on Continue then you notice that the thumbnail is changed and it happens again and again in this Netflix platform. Did you notice ever?

If I talk about the example here, then you have to notice that when you see strangers  things then how many times thumbnail changes. So why thumbnail change in this and how is it working, what is the reason for changing thumbnails many times in Strangers things, I’m going to tell you about this. 

As you all know that Thumbnail is the Only thing that makes the user’s more attractive. It is a Strategy to let’s keep the interest of people watching that series. Now let’s talk about how this will perform in Netflix Machine learning is also used in this to perform this task with the help of A/B testing.

Here A/B testing done like this that some users are shown different thumbnails of the same web series, and then it is seen that which web series is being viewed by clicking on thumbnail many times. Which means the number of clicks comes on which thumbnail.

So this method is mostly used for getting most of the users on a trending Web series. And also the users maintain their interest in that web series.

So now let’s talk about the last feature named Top 10 Trending features. The Top 10 trending web series is only for a short time period that means Top 10 trending web series always update Frequently, Which web series will be in the Top 10 trending this will be decided with the help of Ranking and Rating of that web series which is done by the User’s on Netflix platform.

It is seen how many times that movie, web series have been seen in 24hours and how many of the Users may like that web series  And how many active users saw that web series frequently on Netflix. How many numbers of active users have come in 24hours on that web series?

And one more important thing how many minutes has that web series been watched.

These are some of the factors that decide which web series should be kept in Top Trending, which top web series shown in the Top trending.

I hope you like this if yes then please share this information with your friends.


Author – Monika Prajapat

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