| Document Type
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BL
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| Record Number
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954432
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| Doc. No
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b708802
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| Main Entry
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Liu, Tie-Yan.
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| Title & Author
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Learning to rank for information retrieval /\ Tie-Yan Liu.
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| Publication Statement
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Boston, MA :: Now Pub,, ©2009.
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| Series Statement
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Foundations and trends in information retrieval,; v. 3, issue 3, p. 225-331
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| Page. NO
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1 online resource (ix, 110 pages) :: illustrations
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| ISBN
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1601982453
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: 9781601982452
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1601982445
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9781601982445
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| Bibliographies/Indexes
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Includes bibliographical references (pages 103-110).
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| Contents
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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.
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| Abstract
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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.
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| Subject
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Computer algorithms.
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| Subject
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Information retrieval.
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Information storage and retrieval systems.
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Computer algorithms.
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COMPUTERS-- Programming-- Open Source.
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COMPUTERS-- Software Development Engineering-- General.
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| Subject
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COMPUTERS-- Software Development Engineering-- Tools.
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| Subject
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Information retrieval.
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| Subject
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Information storage and retrieval systems.
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| Dewey Classification
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005.1
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| LC Classification
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QA76.9.A43.L58 2009eb
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