Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational 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
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
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An efficient boosting algorithm for combining preferences
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Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Manifold-ranking based image retrieval
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Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
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Self-taught learning: transfer learning from unlabeled data
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A support vector method for optimizing average precision
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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
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Incremental learning to rank with partially-labeled data
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Semi-supervised document retrieval
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Learning to Rank for Information Retrieval
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A Boosting Approach for Learning to Rank Using SVD with Partially Labeled Data
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Learning to rank only using training data from related domain
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Transductive learning to rank using association rules
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Semi-supervised learning to rank with preference regularization
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Multi-task learning to rank for web search
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Leveraging Auxiliary Data for Learning to Rank
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Robust re-identification using randomness and statistical learning: Quo vadis
Pattern Recognition Letters
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Query-biased learning to rank for real-time twitter search
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A survey of learning to rank for real-time twitter search
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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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Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorithms has focused on cases where only labeled data is available for training (i.e. supervised learning). In this paper, we consider the question whether unlabeled (test) data can be exploited to improve ranking performance. We present a framework for transductive learning of ranking functions and show that the answer is affirmative. Our framework is based on generating better features from the test data (via KernelPCA) and incorporating such features via Boosting, thus learning different ranking functions adapted to the individual test queries. We evaluate this method on the LETOR (TREC, OHSUMED) dataset and demonstrate significant improvements.