User-Based Filtering is a recommendation system technique that suggests items to users based on the preferences of similar users. For example, if User A and User B have similar tastes in movies, and User A enjoyed a particular film, the system may recommend that film to User B. This approach relies on the idea that people with similar interests will appreciate the same items. This method often uses data from user ratings and interactions to identify patterns. By analyzing the preferences of a large group of users, the system can create a personalized experience, helping users discover new content they might like, such as books, music, or products.