Collaborative filtering is an algorithm that creates a recommendation system based on the behavioral data of a user. For instance, online shopping sites usually compile a list of items under “recommended for you” based on your browsing history and previous purchases. The crucial components of this algorithm include users, objects, and their interest.

In the context of data analytics, “Collaborative Filtering” refers to a method used in recommendation systems to make predictions or recommendations about items or preferences based on the preferences or behavior of similar users.

There are two main types of collaborative filtering:

User-based collaborative filtering: This approach recommends items by finding other users who have similar preferences or behavior, and then suggesting items that those similar users have liked or interacted with.
Item-based collaborative filtering: This approach recommends items by identifying similarities between items based on how users have interacted with them in the past. It then suggests items that are similar to those that a user has already liked or interacted with.

Collaborative filtering does not require any knowledge or information about the items being recommended. Instead, it relies solely on the preferences or behavior of users. It is commonly used in recommendation systems for e-commerce platforms, streaming services, and social media platforms to personalize content and improve user experience.