A Complete Recommender System From Scratch in Python: Step by Step

A Complete Recommender System From Scratch in Python: Step by Step

A Complete Recommender System From Scratch in Python: Step by Step. In this article, I will explain a recommender system that used the same idea. I will use some of Python’s libraries like Numpy, Pandas, and Matplotlib for efficient and faster computation.

Nowadays, we see recommendation systems everywhere. When you buy something in an online marketplace like Amazon, eBay, or any other place, they suggest similar products. On Netflix or youtube, you see the suggestions on your homepage similar to your previous activities or searches. How they do it? They all follow this one idea. That is they take data from your previous activities and run a similarity analysis. Based on that analysis they suggest more products or videos or movies you may like.

Overview

In this article, I will explain a recommender system that used the same idea. Here is the list of topic that will be covered here:

  1. The ideas and formulas for the recommendation system.
  2. developing the recommendation system algorithm from scratch
  3. Use that algorithm to recommend movies for me.

I will use some of Python’s libraries like Numpy, Pandas, and Matplotlib for efficient and faster computation. Though our datasets are not too large. But we want to develop something that will work for even bigger datasets. I used a Jupyter Notebook environment. Feel free to use any other notebook of your choice.

How This Recommender System Works?

In this section, I will provide a high-level overview of the process. If it is not totally understandable to you, please keep looking at the next sections. Because in the next sections I will implement all these ideas in python code. So, it will be more clear.

Let’s dive in. Suppose our dataset looks something like this:

Here, we have five movies, four users, and two features. Each user provided some feedback or ratings for each movie. Of course, each user did not watch all the movies. So, sometimes the rating is not available.

In the end, we have two features: Romance and Action. They are giving you an idea about how romantic or how action-packed the movie is. This rating ranges between 0 and 1. 0 romance means no romance and 1 romance means full of romance.

This algorithm will be developed the recommendation system using the user ratings.

If a user watched a lot of movies and rated them, this algorithm will work the best for that user.

But if a certain user did not provide any rating, he or she will get the recommendation based on the other users' ratings.

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