# Adapting boosting for information retrieval measures

title={Adapting boosting for information retrieval measures},
author={Qiang Wu and Christopher J. C. Burges and Krysta Marie Svore and Jianfeng Gao},
journal={Information Retrieval},
year={2010},
volume={13},
pages={254-270}
}
• Published 1 June 2010
• Computer Science
• Information Retrieval
We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely used information retrieval measure. [] Key Method We also show how to find the optimal linear combination for any two rankers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously trained model, and boosting using its residuals, furnishes…
514 Citations
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## References

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SIGIR
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It is shown that LambdaRank, which smoothly approximates the gradient of the target measure, can be adapted to work with four popular IR target evaluation measures using the same underlying gradient construction.
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This work considers the DCG criterion (discounted cumulative gain), a standard quality measure in information retrieval, and proposes using the Expected Relevance to convert the class probabilities into ranking scores.
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This work considers the DCG criterion (discounted cumulative gain), a standard quality measure in information retrieval, and proposes using the Expected Relevance to convert class probabilities into ranking scores.
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