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

TitleCross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Publication TypeConference Paper
Year of Publication2010
AuthorsJing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen
Conference NameInternational Conference on Computational Linguistics
Keywordsmachine-learned ranking, pairwise-trada, search engine ranking
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 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.

Related Files: