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Compact Features for Detection of Near-Duplicates in Distributed Retrieval

  • Yaniv Bernstein
  • Milad Shokouhi
  • Justin Zobel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4209)

Abstract

In distributed information retrieval, answers from separate collections are combined into a single result set. However, the collections may overlap. The fact that the collections are distributed means that it is not in general feasible to prune duplicate and near-duplicate documents at index time. In this paper we introduce and analyze the grainy hash vector, a compact document representation that can be used to efficiently prune duplicate and near-duplicate documents from result lists. We demonstrate that, for a modest bandwidth and computational cost, many near-duplicates can be accurately removed from result lists produced by a cooperative distributed information retrieval system.

Keywords

Hash Function Server Selection Compact Feature Result List Distribute Information Retrieval 
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

  • Yaniv Bernstein
    • 1
  • Milad Shokouhi
    • 1
  • Justin Zobel
    • 1
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia

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