Learning to rank
Learning to rank[1] or machine-learned ranking (MLR) is a type of supervised or semi-supervised machine learning problem in which the goal is to automatically construct a ranking model from training data. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. “relevant” or “not relevant”) for each item. Ranking model’s purpose is to rank, i.e. produce a permutation of items in new, unseen lists in a way, which is “similar” to rankings in the training data in some sense.
