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Publisher
Springer, Berlin, Heidelberg
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Authors: Pavel Metrikov Virgil Pavlu Javed A Aslam
Publish Date: 2013/3/24
Volume: , Issue: , Pages: 760-763
Abstract
We focus on nDCG choice of gains and in particular on the fracture between large differences in exponential gains of high relevance labels and the notsosmall confusion or inconsistency between these labels in data We show that better gains can be derived from data by measuring the label inconsistency to the point that virtually indistinguishable labels correspond to equal gains Our derived optimal gains make a better nDCG objective for training Learning to Rank algorithms
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