Abstract
In this chapter, we introduce the statistical consistency of learning-to-rank methods. In particular, we will introduce the existing results on statistical consistency under different ranking frameworks, and with respect to different true losses, e.g., the pairwise 0–1 loss, the permutation-level 0–1 loss, the top-k loss, and the weighted Kendall’s τ loss. Then we will make discussions on these results and point out the ways of further improving them.
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© 2011 Springer-Verlag Berlin Heidelberg
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Liu, TY. (2011). Statistical Consistency for Ranking. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_18
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DOI: https://doi.org/10.1007/978-3-642-14267-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14266-6
Online ISBN: 978-3-642-14267-3
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