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

ACL ID C10-2003
Title Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

Machine-learned ranking techniques au- tomatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibility required of a commercial web search. However, man- ually labeled training data (with multiple absolute grades) has become the bottle- neck for training a quality ranking func- tion, 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 prefer- ence 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 mar- ket u...