Learning to Rank: Pointwise, Pairwise, Listwise

Learning to rank trains a model to order a list rather than predict a single label. The three formulations are pointwise (predict each item's score on its own), pairwise (predict which of two items ranks higher), and listwise (optimize the whole ordering against a ranking metric). Pairwise and listwise outperform pointwise because they learn relative order, which is what ranking metrics like NDCG actually reward. AI, ML, and GenAI engineer interviews probe it because ranking is the precision stage of search, ads, and recommenders.