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Learning to rank^[1] 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.^[2] Training data may, for example, consist 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 goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.

^ Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval", Foundations and Trends in Information Retrieval, 3 (3): 225–331, doi:10.1561/1500000016, ISBN 978-1-60198-244-5. Slides from Tie-Yan Liu's talk at WWW 2009 conference are available online Archived 2017-08-08 at the Wayback Machine
^ Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT Press ISBN 9780262018258.