The nature of statistical learning theory
The nature of statistical learning theory
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
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
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 25th international conference on Machine learning
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Global ranking by exploiting user clicks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
A risk minimization framework for domain adaptation
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
Adapting boosting for information retrieval measures
Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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Machine-learned ranking functions have shown successes in Web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a big bottleneck, that is, the problem of insufficient labeled training data, which has significantly slowed the development and deployment of machine-learned ranking functions for different domains. There are two possible approaches to address this problem: (1) combining labeled training data from similar domains with the small target-domain labeled data for training or (2) using pairwise preference data extracted from user clickthrough log for the target domain for training. In this article, we propose a new approach called tree-based ranking function adaptation (Trada) to effectively utilize these data sources for training cross-domain ranking functions. Tree adaptation assumes that ranking functions are trained with the Stochastic Gradient Boosting Trees method—a gradient boosting method on regression trees. It takes such a ranking function from one domain and tunes its tree-based structure with a small amount of training data from the target domain. The unique features include (1) automatic identification of the part of the model that needs adjustment for the new domain and (2) appropriate weighing of training examples considering both local and global distributions. Based on a novel pairwise loss function that we developed for pairwise learning, the basic tree adaptation algorithm is also extended (Pairwise Trada) to utilize the pairwise preference data from the target domain to further improve the effectiveness of adaptation. Experiments are performed on real datasets to show that tree adaptation can provide better-quality ranking functions for a new domain than other methods.