Learning To RankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Learning to rank, Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The MIT Press. 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. The ranking model's purpose is to rank, i.e. produce a permutation of items in new, unseen lists in a way that is similar to rankings in the training data in some sense.
