عنوان

Learning to rank for information retrieval /

(Number (ISBN

1601982453

(Number (ISBN

9781601982452

Erroneous ISBN

1601982445

Erroneous ISBN

9781601982445

Number

b708802

Title Proper

Learning to rank for information retrieval /

General Material Designation

[Book]

First Statement of Responsibility

Tie-Yan Liu.

Place of Publication, Distribution, etc.

Boston, MA :

Name of Publisher, Distributor, etc.

Now Pub,

Date of Publication, Distribution, etc.

©2009.

Specific Material Designation and Extent of Item

1 online resource (ix, 110 pages) :

Other Physical Details

illustrations

Series Title

Foundations and trends in information retrieval,

Volume Designation

v. 3, issue 3, p. 225-331

ISSN of Series

1554-0677 ;

Text of Note

Includes bibliographical references (pages 103-110).

Text of Note

Introduction -- The pointwise approach -- The pairwise approach -- The listwise approach -- Analysis of the approaches -- Benchmarking learning-to-rank algorithms -- Statistical ranking theory -- Summary and outlook.

0

Text of Note

Learning to rank for information retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and problems with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seem to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we make a summary and discuss potential future work on learning to rank.

Title

Learning to rank for information retrieval.

International Standard Book Number

1601982445

Computer algorithms.

Information retrieval.

Information storage and retrieval systems.

Computer algorithms.

COMPUTERS-- Programming-- Open Source.

COMPUTERS-- Software Development & Engineering-- General.

COMPUTERS-- Software Development & Engineering-- Tools.

Information retrieval.

Information storage and retrieval systems.

COM-- 051230

COM-- 051390

COM-- 051440

Number

Edition

Class number

Book number

Liu, Tie-Yan.

Date of Transaction

20201207234235.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y