Training with noise is equivalent to Tikhonov regularization
Neural Computation
Machine Learning - Special issue on inductive transfer
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
Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
An empirical study on language model adaptation
ACM Transactions on Asian Language Information Processing (TALIP)
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Language model adaptation with MAP estimation and the perceptron algorithm
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Adapting boosting for information retrieval measures
Information Retrieval
Learning to rank only using training data from related domain
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-market model adaptation with pairwise preference data for web search ranking
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Fractional similarity: cross-lingual feature selection for search
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Query weighting for ranking model adaptation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Clickthrough-based latent semantic models for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Location-aware click prediction in mobile local search
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-task learning to rank for web search
Pattern Recognition Letters
Pairwise cross-domain factor model for heterogeneous transfer ranking
Proceedings of the fifth ACM international conference on Web search and data mining
Learning to rank with multi-aspect relevance for vertical search
Proceedings of the fifth ACM international conference on Web search and data mining
Flexible sample selection strategies for transfer learning in ranking
Information Processing and Management: an International Journal
Example based entity search in the web of data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Personalized ranking model adaptation for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Modeling click-through based word-pairs for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Adapting deep RankNet for personalized search
Proceedings of the 7th ACM international conference on Web search and data mining
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
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This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but its performance drops significantly on the open test sets due to the instability of trees. Several methods are explored to improve the robustness of the algorithm, with limited success.