Preference-based learning

Preference-based learning is a method where a computer system learns to make decisions or recommendations based on how users express their preferences, rather than explicit instructions. Instead of providing specific rules, users give feedback by ranking or choosing between options, allowing the system to understand what is most preferred. Over time, it refines its choices to better match individual tastes or needs. This approach is useful in personalized services, like recommending movies or products, because it adapts to what users like through their subtle feedback, making interactions more intuitive and tailored.