Collaborative Filtering in Machine Learning

Collaborative filtering is a recommendation system method that is formed by the collaboration of multiple users. The idea behind it is to recommend products or services to a user that their peers have appreciated. In this article, I will introduce you to collaborative filtering in machine learning and its implementation using Python.

What is Collaborative Filtering?

Collaborative filtering is a method of recommendation systems. A recommendation system is used to suggest or recommend products and services to users based on their interests and preferences. These are two methods of creating a recommendation system, the other is known as content-based filtering.

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Thus, collaborative filtering is used to create a recommendation system by analyzing the behaviour of multiple users or a single user and recommend products to new users based on the behaviour and characteristics of previous users. It means the automatic collaboration of multiple users and filters out those with similar traits, then recommend a particular service to a particular group of people with similar traits.

Content-based filtering uses the description of the product or service, and collaborative filtering filters a group of people with similar characteristics to recommend products and services.

To create a recommendation system using collaborative filtering, we need to filter the ratings and reviews for that product a customer is looking for. A better example of such a system will be a hotel recommendation system in which we recommend hotels based on clients’ goals and recommend them hotels with the highest ratings by people with similar characteristics.

Machine Learning Project on Collaborative Filtering using Python

This method is used to find correlations between different users and then recommend them the same products based on their similarities. So to implement it using Python, I have presented a hotel recommendation system where you will learn how to recommend hotels to a particular class of people.

For example, if we are going on a business trip then our recommendation system should show the best-rated hotels by the customers who went on a business trip. Similarly, if a couple is going for a honeymoon then the recommendation system should recommend the best-rated hotels by the customers who went on honeymoon. So you can learn how to create such a recommendation system using Python from below.

I hope you liked this article on what is collaborative filtering in machine learning. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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