Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking

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, PairwiseTrada, 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 pairwise preference data. We present results demonstrating the efficacy of our technique on a set of commercial search engine data.