Collaborative Filtering is a technique used in recommendation systems to suggest items based on the preferences of similar users. For example, if you and a friend both enjoy action movies, the system might recommend a new action film that your friend liked, even if you haven't seen it yet. This method relies on the idea that people with similar tastes will appreciate similar items. There are two main types of Collaborative Filtering: user-based and item-based. User-based filtering focuses on finding users with similar preferences, while item-based filtering looks at the relationships between items. Both approaches help platforms like Netflix and Amazon provide personalized recommendations, enhancing user experience.