CLEF 2005: multilingual retrieval by combining multiple multilingual ranked lists
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Top-k dominant web services under multi-criteria matching
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Effective query generation and postprocessing strategies for prior art patent search
Journal of the American Society for Information Science and Technology
Slow Search: Information Retrieval without Time Constraints
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
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Federated search is the task of retrieving relevant documents from different information resources. One of the main research problems in federated search is to combine the results from different sources into a single ranked list. Recent work proposed a regression based method to download some documents from each ranked list of the different sources, calculated comparable scores for the documents and estimated mapping functions that transform source-specific scores into comparable scores. Experiments have shown that downloading more documents improves the accuracy of results merging. However downloading more documents increases the computation and communication costs. This paper proposes a utility based optimization method that enables the system to automatically decide on the desired number of training documents to download according to the user's need for effectiveness and efficiency.