User-Based Collaborative Filtering is a recommendation technique that suggests items to users based on the preferences of similar users. It operates on the principle that if two users have a history of liking similar items, they are likely to enjoy the same items in the future. This method relies on user ratings and behaviors to identify patterns and make personalized recommendations. To implement User-Based Collaborative Filtering, systems typically create a user-item matrix that tracks user preferences. Algorithms then calculate similarities between users, often using metrics like cosine similarity or Pearson correlation. The most similar users' preferences are then used to recommend new items to a target user.