  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 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.
