Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Distributed search over the hidden web: hierarchical database sampling and selection
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Generative model-based metasearch for data fusion in information retrieval
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search. The lack of features affects the efficiency of collection selection in distributed search. In this paper, we exploit some new features and learn to rank collections with them through SVM and RankingSVM respectively. Experimental results show that our features are beneficial to collection selection, and the learned ranking functions outperform the classical CORI algorithm.