Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised grammar induction using training data with limited constituent information
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
TransRank: A Novel Algorithm for Transfer of Rank Learning
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Ranking model adaptation for domain-specific search
Proceedings of the 18th ACM conference on Information and knowledge management
Multi-task learning for learning to rank in web search
Proceedings of the 18th ACM conference on Information and knowledge management
On domain similarity and effectiveness of adapting-to-rank
Proceedings of the 18th ACM conference on Information and knowledge management
Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions
Proceedings of the 18th ACM conference on Information and knowledge management
Model adaptation via model interpolation and boosting for web search ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Empirical exploitation of click data for task specific ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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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.