On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Modern Information Retrieval
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Ranking with ordered weighted pairwise classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatic text summarization based on word-clusters and ranking algorithms
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
LIP6 at INEX'10: OWPC for ad hoc track
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
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We present a Retrieval Information system for XML documents using a Machine Learning Ranking approach. We then propose a way to annotate and build a training set for our learning to rank algorithm OWPC [1]. Finally, we apply our algorithm to the INEX'09 collection and present the results we obtained.