Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
IntervalRank: isotonic regression with listwise and pairwise constraints
Proceedings of the third ACM international conference on Web search and data mining
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We describe our experiences in applying learning-to-rank techniques to improving the quality of search results of an online hotel reservation system. The search result quality factors we use are average booking position and distribution of margin in top-ranked results. (We expect that total revenue will increase with these factors.) Our application of the SVMRank technique improves booking position by up to 25% and margin distribution by up to 14%.