Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
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 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
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
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Learning to rank is one of the most hot research areas in information retrieval, among which listwise approach is an important research direction and the methods that directly optimizing evaluation metrics in listwise approach have been used for optimizing some important ranking evaluation metrics, such as MAP, NDCG and etc. In this paper, the structural SVMs method is employed to optimize the Expected Reciprocal Rank(ERR) criterion which is named SVMERR for short. It is compared with state-of-the-art algorithms. Experimental results show that SVMERR outperforms other methods on OHSUMED dataset and TD2003 dataset, which also indicate that optimizing ERR criterion could improve the ranking performance.