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learning to rank
Learning to rank〔. Slides from Tie-Yan Liu's talk at WWW 2009 conference are (available online )
〕 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.〔Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The
MIT Press ISBN 9780262018258.〕 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 which is "similar" to rankings in the training data in some sense.
Learning to rank is a relatively new research area which has emerged in the past decade.
== Applications ==
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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