NDLI: Structured learning for non-smooth ranking losses

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Structured learning for non-smooth ranking losses

Content Provider	ACM Digital Library
Author	Khanna, Rajiv Chakrabarti, Soumen Sawant, Uma Bhattacharyya, Chiru
Abstract	Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP (mean average precision). We propose new, almost-linear-time algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain) in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization. The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.
Starting Page	88
Ending Page	96
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401906
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Max-margin structured learning to rank Non-decomposable loss functions
Content Type	Text
Resource Type	Article

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