Collaborative filtering is a method used by recommendation systems, particularly in the field of online shopping, streaming platforms, and social media. Its fundamental premise is based on the idea that users who agreed in the past are likely to agree again in the future. In simple terms, it suggests items to a user based on the preferences of other users who have similar taste or behavior.
This technique can further be categorized into two types: user-based and item-based collaborative filtering. User-based collaborative filtering determines the likeliness of a product for a user based on the ratings given by users who have similar tastes. On the other hand, item-based collaborative filtering recommends items by comparing the set of items a user has rated with the set of items that other users have rated.
However, despite its utility, collaborative filtering comes with its set of challenges. Notably, the ‘cold start problem,’ whereby the algorithm struggles to make a recommendation when it encounters a new user or a new item without prior ratings. There are also issues associated with scalability, given that a large number of users and items can significantly impact the computation resources. Regardless of these challenges, collaborative filtering remains an essential technique in machine learning for personalization and recommendation.