Paper: Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

ACL ID D09-1053
Title Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

This paper explores two classes of model adapta- tion methods for Web search ranking: Model In- terpolation 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 sim- ilar, but its performance drops significantly on the open test sets due to the instability of trees. Several methods are explored to improve the robustness of the algorithm, with limited success.