Recommender systems are algorithms designed to suggest products, services, or content to users based on their preferences and behaviors. They analyze data from user interactions, such as ratings, purchases, or browsing history, to identify patterns and make personalized recommendations. Common examples include movie suggestions on Netflix and product recommendations on Amazon. There are two main types of recommender systems: collaborative filtering and content-based filtering. Collaborative filtering relies on the preferences of similar users to make suggestions, while content-based filtering uses the characteristics of items to recommend similar ones. Both methods aim to enhance user experience by providing relevant options.