Rank-based approach in machine learning

A rank-based approach in machine learning involves organizing data or predictions according to their relative importance or performance, rather than absolute values. For example, instead of looking at the actual scores or probabilities, the focus is on the order or ranking of items based on specific criteria. This method is useful in scenarios like recommendation systems, where users are presented with items ranked by relevance. By prioritizing ranks, this approach can enhance decision-making and improve user experience by highlighting the best options tailored to an individual's preferences.