Learning to Rank
A class of supervised machine learning techniques designed to optimize the ordering of a list of items rather than predicting an exact score or class.
Think of It Like This
Like a DJ arranging a playlist so the best songs play first, rather than just rating each song individually.
This approach uses algorithms like RankNet or LambdaMART to minimize ranking-specific loss functions. It is the fundamental technology behind search engine results, product recommendations, and social media feeds. The models are trained on lists of documents annotated with graded relevance scores.