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Ranking with Ordered Weighted Pairwise Classification
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BibTeX
@MISC{Usunier_rankingwith,
author = {Nicolas Usunier and David Buffoni and Patrick Gallinari},
title = {Ranking with Ordered Weighted Pairwise Classification},
year = {}
}
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Abstract
In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to optimize a larger class of loss functions for ranking, based on an ordered weighted average (OWA) (Yager, 1988) of the classification losses. Convex OWA aggregation operators range from the max to the mean depending on their weights, and can be used to focus on the top ranked elements as they give more weight to the largest losses. When aggregating hinge losses, the optimization problem is similar to the SVM for interdependent output spaces. Moreover, we show that OWA aggregates of marginbased classification losses have good generalization properties. Experiments on the Letor 3.0 benchmark dataset for information retrieval validate our approach. 1.
Keyphrases
ordered weighted pairwise classification ranked list top ranked element loss function pairwise classification approach marginbased classification loss weighted average hinge loss optimization problem owa aggregate information retrieval validate convex owa aggregation operator classification loss interdependent output space benchmark dataset good generalization property information retrieval pairwise classification loss high precision
