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Presentation
Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Proceedings of the 23rd International Conference on Computational Linguistics
  • Jing Bai
  • Fernando Diaz
  • Yi Chang
  • Zhaohui Zheng
  • Keke Chen, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
8-1-2010
Abstract
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.
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Presented a the 23rd International Conference on Computational Linguistics, Beijing, China, August 23-27, 2010.

Citation Information
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, et al.. "Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking" Proceedings of the 23rd International Conference on Computational Linguistics (2010) p. 18 - 26 ISSN: 9787302234562
Available at: http://works.bepress.com/keke_chen/14/