|Title||Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking|
|Publication Type||Conference Paper|
|Year of Publication||2010|
|Authors||Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen|
|Conference Name||International Conference on Computational Linguistics|
|Keywords||machine-learned ranking, pairwise-trada, search engine 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, 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 pairwise preference data. We present results demonstrating the efficacy of our technique on a set of commercial search engine data.
|Full Text|| |
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng and Keke Chen, ' Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking ', International Conference on Computational Linguistics (COLING), 2010.