What systems are created to recommend products, services, or content to users based on their behavior and profile?

Recommendation systems are designed to analyze user data, including past behaviors and profiles, to suggest products, services, or content that align with individual preferences. These systems use various algorithms to predict what users might be interested in, utilizing techniques such as collaborative filtering or content-based filtering. By personalizing the experience, recommendation systems enhance user engagement and satisfaction by providing tailored suggestions, making them a fundamental component of many online platforms.

While classification systems focus on categorizing data into predefined classes, and search algorithms are primarily about retrieving relevant information based on queries, recommendation systems uniquely focus on anticipating users' needs and interests. Data mining systems, on the other hand, involve extracting patterns and insights from large datasets but do not specifically target the act of recommending based on individual user behavior or profiles. This distinct functionality of recommendation systems is what sets them apart in the context of supporting user interactions and driving engagement in digital environments.