Probabilistic Score Normalization for Rank Aggregation

  • Miriam Fernández
  • David Vallet
  • Pablo Castells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


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.


Information Retrieval Score Distribution Rank List Rank Aggregation Individual Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miriam Fernández
    • 1
  • David Vallet
    • 1
  • Pablo Castells
    • 1
  1. 1.Escuela Politécnica SuperiorUniversidad Autónoma de MadridCiudad Universitaria de Cantoblanco, MadridSpain

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