Rank-based learning

Rank-based learning is a method in machine learning where models are trained to order items correctly, rather than just classify them. Instead of focusing on labeling each item as right or wrong, the model learns to rank or prioritize items based on their relevance or importance. For example, in search engines, it helps排序 the most relevant results higher. This approach emphasizes the relative positioning of items, improving the quality of rankings rather than just accuracy. It’s particularly useful for tasks like recommendation systems, search result ranking, and information retrieval.