Reducing the Uncertainty in Resource Selection

  • Ilya Markov
  • Leif Azzopardi
  • Fabio Crestani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)

Abstract

The distributed retrieval process is plagued by uncertainty. Sampling, selection, merging and ranking are all based on very limited information compared to centralized retrieval. In this paper, we focus our attention on reducing the uncertainty within the resource selection phase by obtaining a number of estimates, rather than relying upon only one point estimate. We propose three methods for reducing uncertainty which are compared against state-of-the-art baselines across three distributed retrieval testbeds. Our results show that the proposed methods significantly improve baselines, reduce the uncertainty and improve robustness of resource selection.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ilya Markov
    • 1
  • Leif Azzopardi
    • 2
  • Fabio Crestani
    • 1
  1. 1.University of LuganoLuganoSwitzerland
  2. 2.University of GlasgowGlasgowUK

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