document: Structured learning for non-smooth ranking losses 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 .
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