Recommendation engines function in digital platforms by analyzing user behavior and preferences to suggest personalized content or products. They utilize various algorithms, including collaborative filtering and content-based filtering. Collaborative filtering considers the interactions of similar users to predict what another user might like, while content-based filtering focuses on the characteristics of items themselves. These engines enhance user experience by streamlining the decision-making process, increasing engagement, and boosting sales. By providing tailored recommendations, digital platforms can create a more personalized environment for users, encouraging them to explore and discover relevant content.