Automatic text processing
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in 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
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in 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
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
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Decomposing background topics from keywords by principal component pursuit
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Direct optimization of ranking measures for learning to rank models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning to rank in document retrieval. Learning-based methods, such as RankNet, RankSVM, and RankBoost, try to create ranking functions automatically by using some training data. Recently, several learning to rank methods have been proposed to directly optimize the performance of IR applications in terms of various evaluation measures. They undoubtedly provide statistically significant improvements over conventional methods; however, from the viewpoint of decision-making, most of them do not minimize the Bayes risk of the IR system. In an attempt to fill this research gap, we propose a novel framework that directly optimizes the Bayes risk related to the ranking accuracy in terms of the IR evaluation measures. The results of experiments on the LETOR collections demonstrate that the framework outperforms several existing methods in most cases.