Item-Based Filtering is a recommendation technique used in systems like Netflix or Amazon to suggest products or content based on the similarities between items. Instead of focusing on user preferences, this method analyzes the relationships between items themselves. For example, if a user enjoys a particular movie, the system will recommend other movies that are similar in genre, theme, or style. This approach relies on the idea that if two items are frequently liked or purchased together, they are likely to appeal to similar audiences. By examining patterns in user behavior, item-based filtering helps create personalized experiences, enhancing user satisfaction and engagement.