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The goodness of a model is usually evaluated with performance measures such as MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain). Ideally a learning algorithm would train a ranking model that could directly optimize the performance measures with respect to the training data. Existing methods, however, are only able to train ranking models by minimizing loss functions loosely related to the performance measures. To deal with the problem, this paper proposes a novel learning algorithm within the framework of boosting, which can minimize a loss function directly defined on the performance measures. The algorithm, referred to as AdaRank, repeatedly constructs 'Weak rankers' on the basis of re-weighted training data and finally linearly combines the weak rankers for making ranking predictions.

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Format:PDFSize:833 KB
Date:May 2007
Pages:8

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