Hybrid recommendation systems combine multiple techniques to suggest items to users. They typically integrate collaborative filtering, which analyzes user behavior and preferences, with content-based filtering, which focuses on the attributes of items. This approach helps overcome limitations of individual methods, such as the cold start problem, where new users or items lack sufficient data. By leveraging both user interactions and item characteristics, hybrid systems can provide more accurate and personalized recommendations. They are widely used in various applications, including e-commerce, streaming services, and social media, enhancing user experience and engagement through tailored suggestions.