Preference Learning is a type of machine learning that focuses on understanding and predicting the preferences of individuals or groups. It involves creating models that can rank or order items based on the preferences expressed by users. This approach is commonly used in recommendation systems, where the goal is to suggest products, services, or content that align with a user's tastes. In Preference Learning, algorithms analyze data from user interactions, such as ratings or choices, to identify patterns. These models can then be used to make personalized recommendations, improving user satisfaction and engagement. Techniques like Collaborative Filtering and Ranked Learning are often employed to enhance the accuracy of these predictions.