Reducing the uncertainty in resource selection

Ilya Markov, Leif Azzopardi, Fabio Crestani

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

12 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings
Place of PublicationBerlin, Heidelberg
PublisherSpringer-Verlag
Pages507-519
Number of pages13
Volume7814
ISBN (Print)978-3-642-36972-8
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer-Verlag
Volume7814
ISSN (Print)0302-9743

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Keywords

  • distributed information retrieval
  • uncertainty in search
  • information seeking behaviour
  • resource discovery

Cite this

Markov, I., Azzopardi, L., & Crestani, F. (2013). Reducing the uncertainty in resource selection. In Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings (Vol. 7814, pp. 507-519). (Lecture Notes in Computer Science; Vol. 7814). Berlin, Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-36973-5_43