Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. The most important components of collaborative filtering are users- items- interest.
A good example of collaborative filtering is when you see a statement like “recommended for you” on online shopping sites that’s pops out based on your browsing history.
Collaborative filtering is a type of recommendation system technique used in data analytics and machine learning. It works by making automatic predictions (filtering) about the interests of a user by collecting preferences from many users (collaborating).
There are two main types of collaborative filtering:
- User-based collaborative filtering: This method identifies users who are similar to the target user and then recommends items that they have liked or purchased. The idea is that if two users have similar tastes or preferences, one user is likely to enjoy items that the other user has liked. This approach relies on user-item interactions.
- Item-based collaborative filtering: Instead of comparing users, this method identifies items that are similar to the items the target user has liked or interacted with. The system then recommends items that are similar to those already preferred by the user. Item-based collaborative filtering can be computationally less expensive than user-based collaborative filtering and can perform better in scenarios where the number of users is much larger than the number of items.
Both methods require a substantial amount of user-item interaction data to train the model effectively. Collaborative filtering systems are widely used in recommendation engines for e-commerce platforms, content streaming services, social media platforms, and more, to personalize recommendations for users based on their past behavior and preferences.