Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

22 Jul 2019Jianfeng GaoQiang WuChris BurgesKrysta SvoreYi SuNazan KhanShalin ShahHongyan Zhou

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... (read more)

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