Livre: Li, Learning to Rank for IR and NLP

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Fri Aug 12 18:53:31 UTC 2011

Date: Fri, 12 Aug 2011 11:34:07 -0400
From: Graeme Hirst <gh at>
Message-Id: <33A0FD2E-4D3D-43D0-8E9F-C75348EA2EBB at>


Learning to Rank for Information Retrieval and Natural Language Processing
Hang Li
April 2011
PDF (3563 KB) | PDF Plus (1635 KB) 

Learning to rank refers to machine learning techniques for training the
model in a ranking task. Learning to rank is useful for many
applications in information retrieval, natural language processing, and
data mining. Intensive studies have been conducted on the problem
recently and significant progress has been made. This lecture gives an
introduction to the area including the fundamental problems, existing
approaches, theories, applications, and future work.

The author begins by showing that various ranking problems in
information retrieval and natural language processing can be formalized
as two basic ranking tasks, namely ranking creation (or simply ranking)
and ranking aggregation. In ranking creation, given a request, one wants
to generate a ranking list of offerings based on the features derived
from the request and the offerings. In ranking aggregation, given a
request, as well as a number of ranking lists of offerings, one wants to
generate a new ranking list of the offerings.

Ranking creation (or ranking) is the major problem in learning to
rank. It is usually formalized as a supervised learning task. The author
gives detailed explanations on learning for ranking creation and ranking
aggregation, including training and testing, evaluation, feature
creation, and major approaches. Many methods have been proposed for
ranking creation. The methods can be categorized as the pointwise,
pairwise, and listwise approaches according to the loss functions they
employ. They can also be categorized according to the techniques they
employ, such as the SVM based, Boosting SVM, Neural Network based

The author also introduces some popular learning to rank methods in
details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank,
RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank,
Borda Count, Markov Chain, and CRanking.

The author explains several example applications of learning to rank
including web search, collaborative filtering, definition search,
keyphrase extraction, query dependent summarization, and re-ranking in
machine translation.

A formulation of learning for ranking creation is given in the
statistical learning framework. Ongoing and future research directions
for learning to rank are also discussed.

Table of Contents: Introduction / Learning for Ranking Creation /
Learning for Ranking Aggregation / Methods of Learning to Rank /
Applications of Learning to Rank / Theory of Learning to Rank / Ongoing
and Future Work

This title is available online free of charge to members of institutions
that that have licensed through the Synthesis Digital Library of
Engineering and Computer Science. Use of this book as a course text is
encouraged; and the text may be downloaded without restriction at
licensing institutions, or after a one-time fee of $30 USD at
non-licensing schools. To find out whether your institution is a
subscriber, visit, or
follow the links above and attempt to download the PDF. Additional
information about Synthesis can be found through the following links, or
by contacting me directly. 

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A review of Synthesis in ISTL:

This book can also be purchased in print directly from the Morgan &
Claypool Bookstore for $35.00 USD, or from Amazon and other booksellers

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