A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
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
Two-stage language models for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Linear discriminant model for information retrieval
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)
On rank-based effectiveness measures and optimization
Information Retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
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
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Language model adaptation with MAP estimation and the perceptron algorithm
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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
The anatomy of an ad: structured indexing and retrieval for sponsored search
Proceedings of the 19th international conference on World wide web
Classification-enhanced ranking
Proceedings of the 19th international conference on World wide web
CADRA: context aware data retrieval architecture
International Journal of Advanced Intelligence Paradigms
Learning to rank with multiple objective functions
Proceedings of the 20th international conference on World wide web
Fractional similarity: cross-lingual feature selection for search
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Learning to rank for freshness and relevance
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Post-ranking query suggestion by diversifying search results
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A robust ranking methodology based on diverse calibration of AdaBoost
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Characterizing web content, user interests, and search behavior by reading level and topic
Proceedings of the fifth ACM international conference on Web search and data mining
Robust ranking models via risk-sensitive optimization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
Learning to rank for spatiotemporal search
Proceedings of the sixth ACM international conference on Web search and data mining
Two-Stage learning to rank for information retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Fighting search engine amnesia: reranking repeated results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Direct optimization of ranking measures for learning to rank models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Relevance in microblogs: enhancing tweet retrieval using hyperlinked documents
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Non-reference audio quality assessment for online live music recordings
Proceedings of the 21st ACM international conference on Multimedia
Learning relatedness measures for entity linking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
User modeling in search logs via a nonparametric bayesian approach
Proceedings of the 7th ACM international conference on Web search and data mining
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We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely used information retrieval measure. Our algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly faster in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two rankers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously trained model, and boosting using its residuals, furnishes an effective technique for model adaptation, and we give significantly improved results for a particularly pressing problem in web search--training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.