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
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
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In this paper, we propose a sparse Bayesian approach to learn ranking function from labeled data. The ranking function can be used to define an ordering among documents according to their degree of relevance to the user query. This ranking function is more efficient and accurate than the function leaned by proposed approaches. Experimental results on document retrieval dataset show that the generalization performance of it is competitive with SVM-based ranking method and Gaussian process based method.