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 independently), pairwise (predict which of two items ranks higher), and listwise (optimize the whole ordering against a ranking metric). Pairwise and listwise beat pointwise because they learn relative order, which is what ranking metrics like NDCG actually reward. Applied-AI interviews probe it because ranking is the precision stage of search, ads, and recommenders.