👉 Prefer computing, also known as preference learning or preference-based machine learning, is a subfield of artificial intelligence that focuses on learning from users' preferences rather than explicit feedback or labels. In traditional machine learning, models are trained on labeled data where the correct output is provided for each input. However, in prefer computing, the model learns from implicit or explicit preference signals given by users, such as ratings, rankings, or even non-verbal cues. This approach is particularly useful when obtaining explicit feedback from users is difficult, costly, or time-consuming. Prefer computing aims to infer user preferences by analyzing their interactions with different options and adjusting the model's predictions accordingly. This enables systems to better align with user needs, improve personalization, and enhance decision-making processes in various applications, including recommendation systems, product design, and user experience optimization.
