Recommender systems are a type of information filtering system that predicts a user's preferences or interests in a particular item or category, and provides personalized recommendations based on those preferences. These systems are commonly used in online platforms such as e-commerce websites, social media platforms, and streaming services to suggest products, content, or services that users may be interested in. There are different types of recommender systems, including collaborative filtering, content-based filtering, and hybrid methods that combine aspects of both approaches. Collaborative filtering algorithms analyze user behavior and preferences to identify patterns and recommend items that are popular among similar users. Content-based filtering, on the other hand, recommends items based on the characteristics of the items themselves and how closely they match a user's past preferences. Recommender systems rely on data mining and machine learning techniques to analyze user data and make accurate predictions. These systems have become essential tools for businesses seeking to enhance their customer experience and increase sales by providing personalized recommendations to users.