Fernandez, Miriam; Vallet, David and Castells, Pablo
Using historical data to enhance rank aggregation.
In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, p. 643.
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Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consistently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal.
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