Resource type

Learning to Rank for Information Retrieval

Resource type
Author/contributor
Title
Learning to Rank for Information Retrieval
Abstract
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 disadvantages 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 seems 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 provide a summary and discuss potential future work on learning to rank.
Publication
Foundations and TrendsĀ® in Information Retrieval
Volume
3
Issue
3
Pages
225-331
Date
2009/6/27
Journal Abbr
INR
Language
en
DOI
10.1561/1500000016
ISSN
1554-0669, 1554-0677
Accessed
2019-01-18T20:05:20Z
Library Catalog
Citation
Liu, T.-Y. (2009). Learning to Rank for Information Retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225–331. https://doi.org/10.1561/1500000016
Field of study
Contribution