Home > Interesting, python > Creating a Simple Recommender System in Python using Pandas

Creating a Simple Recommender System in Python using Pandas

Have you ever wondered how Netflix suggests movies to you based on the movies you have already watched? Or how does an e-commerce websites display options such as “Frequently Bought Together”? They may look relatively simple options but behind the scenes, a complex statistical algorithm executes in order to predict these recommendations. Such systems are called Recommender Systems, Recommendation Systems, or Recommendation Engines. A Recommender System is one of the most famous applications of data science and machine learning.

A Recommender System employs a statistical algorithm that seeks to predict users’ ratings for a particular entity, based on the similarity between the entities or similarity between the users that previously rated those entities. The intuition is that similar types of users are likely to have similar ratings for a set of entities.

Currently, many of the big tech companies out there use a Recommender System in one way or another. You can find them anywhere from Amazon (product recommendations) to YouTube (video recommendations) to Facebook (friend recommendations). The ability to recommend relevant products or services to users can be a huge boost for a company, which is why it’s so common to find this technique employed in so many sites.

In this article, we will see how we can build a simple recommender system in Python.



  1. No comments yet.
  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.

%d bloggers like this: