Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Svore, Yi Su, Nazan Khan, Shalin Shah, and Hongyan Zhou

Abstract

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpola-tion and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best re-sults 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 its perfor-mance drops significantly on the open test sets due to the instability of trees. Several methods are ex-plored to improve the robustness of the algorithm, with limited success.

Details

Publication typeInproceedings
Published inEMNLP
PublisherAssociation for Computational Linguistics
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