Netflix is the latest buzzword in the entertainment industry. If you are a Netflix user then you must have surely noticed that most of the movies or web series recommendations on Netflix would surely be of your interest. Every wondered how Netflix know what you’d like to watch & come up with appropriate recommendations most of the time?

The answer for this question is simply Data Science. Netflix & other content management websites are largely relying on Data Science &its use cases to cater relevant and interesting recommendations to their users. In this blog, let’s start exploring the role of Data Science in Netflix.  

Netflix also makes use of Hybrid Recommendation system to deliver the best content to its users. Get to know more about how Data Science is being used by the other content providers like YouTube, Spotify & others by joining for our Kelly Technologies advanced Data Science Training in Hyderabad.

Data Science At Netflix-

Netflix is a major breakthrough in the entertainment industry, which was initially started as a DVD rental service in 1998. Having experienced a lot of failures for a long period time, it has finally come up with the idea of delivering online streaming service in 2007. Netflix has made a lot of investments to deliver its users with flawless movie experience. It has started implementing a lot of algorithms in this regard & one of such algorithms is the Recommendation System.

Recommendation system analyzes the users data & comes up with suggestions by analyzing the patterns in the data. These suggestions include movie recommendations, web series recommendations or any other it recommends any other cinematographic products that would be of the users’ interest. This Recommender System is an advanced concept in Data Science.

Types Of Recommendation Systems-

There are two different types of Recommendation Systems namely

Content-Based Recommendation Systems-

This system takes into account background knowledge of products and customer information. Whatever content user has previously viewed on Netflix, by analyzing this data, it presents the user with suggestions that are similar to the ones which he has earlier watched.

Collaborative Filtering Recommendation Systems-

Unlike the content based recommendation system, this collaborative filtering recommendation system works towards presenting recommendations based on the similar profiles of its users. To explain this, let’s consider two persons A & B. If person A watches romcom, action & scifi movies B watches sci-fi, action & and thriller genres then A will also like thriller and B will like romcom genre.

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