Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

@inproceedings{Gao2009ModelAV,
  title={Model Adaptation via Model Interpolation and Boosting for Web Search Ranking},
  author={Jianfeng Gao and Qiang Wu and C. Burges and K. Svore and Yi Su and N. Khan and Shalin S Shah and Hongyan Zhou},
  booktitle={EMNLP},
  year={2009}
}
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but… Expand
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