Learning to Rank for Information Retrieval
Foundations and Trends® in
Information Retrieval
Volume 3 Issue 3
DOI: 10.1561/1500000016
Learning to Rank for Information Retrieval
Tie-Yan Liu
Microsoft Research Asia, Sigma Center, No. 49, Zhichun Road, Haidian
District, Beijing, 100190, P. R. China, Tie-Yan.Liu@microsoft.com
SUGGESTED CITATION:
Tie-Yan
Liu
(2009)
"Learning to Rank for Information Retrieval",
Foundations and Trends® in Information Retrieval: Vol. 3: No 3, pp 225-331.
http:/dx.doi.org/10.1561/1500000016
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.