Adapting Boosting for Information Retrieval Measures

Qiang Wu,Christopher J. C. Burges,Krysta M. Svore,Jianfeng Gao

Information Retrieval（2009）

Cited 403|Views17

Abstract

We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely used information retrieval measure. Our algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly faster in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two rankers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously trained model, and boosting using its residuals, furnishes an effective technique for model adaptation, and we give significantly improved results for a particularly pressing problem in web search--training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.

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Key words

Learning to rank,Boosting,Web search

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
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The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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