Introduction

A recommendation system generates a compiled list of items in which a user might be interested, in the reciprocity of their current selection of item(s). It expands users’ suggestions without any disturbance or monotony, and it does not recommend items that the user already knows.

For instance, the Netflix recommendation system offers recommendations by matching and searching similar users’ habits and suggesting movies that share characteristics with films that users have rated highly.

In this tutorial, we will dive into building a recommendation system for Netflix.

This tutorial’s code is available on Github and its full implementation as well on Google Colab.

The recommendation system workflow shown in the diagram above shows the user’s collaboration regarding the ratings of different movies or shows. New users get their recommendations based on the recommendations of existing users.

According to McKinsey:

75% of what people are watching on Netflix comes from recommendations [1].

Netflix Real-time data cases:

• More than 20,000 movies and shows.
• 2 million users.

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