Item-Based Collaborative Filtering is a recommendation technique that analyzes the relationships between items based on user preferences. Instead of focusing on users, it looks at how similar items are rated by different users. For example, if users who liked Item A also liked Item B, the system can recommend Item B to someone who enjoyed Item A. This method is widely used in various applications, such as e-commerce and streaming services, to enhance user experience. By identifying patterns in item ratings, it helps users discover new products or content that align with their interests, improving engagement and satisfaction.