@inproceedings{d6810d8c011c47f6848e0df4f649e433,
title = "Preference learning to rank with sparse Bayesian",
abstract = "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 SVMbased ranking method and Gaussian process based method.",
keywords = "Information retrieval, Learning to rank, Sparse Bayesian",
author = "Xiao Chang and Qinghua Zheng",
year = "2009",
doi = "10.1109/WI-IAT.2009.367",
language = "英语",
isbn = "9780769538013",
series = "Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009",
pages = "143--146",
booktitle = "Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009",
note = "2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009 ; Conference date: 15-09-2009 Through 18-09-2009",
}