# Adapting boosting for information retrieval measures

@article{Wu2010AdaptingBF, 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} }

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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