Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Learning to rank for information retrieval (LR4IR 2007)
ACM SIGIR Forum
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Pseudo test collections for learning web search ranking functions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Learning to rank has become a hot issue in the community of information retrieval. It combines the relevance judgment information with the approaches of both in information retrieval and machine learning, so as to learn a more accurate ranking function for retrieval. Most previous approaches only rely on the labeled relevance information provided, thus suffering from the limited training data size available. In this paper, we try to use Singular Value Decomposition (SVD) to utilize the unlabeled data set to extract new feature vectors, which are then embedded in a RankBoost leaning framework. We experimentally compare the performance of our approach against that without incorporating new features generated by SVD. The experimental results show that our approach can consistently improve retrieval performance across several LETOR data sets, thus indicating effectiveness of new SVD generated features for learning ranking function.