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Conference Proceeding

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Machine-learned ranking techniques automatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibility required of a commercial web search. However, manually labeled training data (with multiple absolute grades) has become the bottleneck for training a quality ranking function, particularly for a new domain. In this paper, we explore the adaptation of machine-learned ranking models across a set of geographically diverse markets with the market-specific pairwise preference data, which can be easily obtained from clickthrough logs. We propose a novel adaptation algorithm, Pairwise-Trada, which is able to adapt ranking models that are trained with multi-grade labeled training data to the target market using the target-market-specific pair-wise preference data. We present results demonstrating the efficacy of our technique on a set of commercial search engine data.


Presented a the 23rd International Conference on Computational Linguistics, Beijing, China, August 23-27, 2010.