Probabilistic score normalization for rank aggregation

  • Authors:
  • Miriam Fernández;David Vallet;Pablo Castells

  • Affiliations:
  • Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain

  • Venue:
  • ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
  • Year:
  • 2006

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Abstract

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.