Learning to Rank from Relevance Feedback for e-Discovery

  • Peter Lubell-Doughtie
  • Katja Hofmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

In recall-oriented search tasks retrieval systems are privy to a greater amount of user feedback. In this paper we present a novel method of combining relevance feedback with learning to rank. Our experiments use data from the 2010 TREC Legal track to demonstrate that learning to rank can tune relevance feedback to improve result rankings for specific queries, even with limited amounts of user feedback.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Peter Lubell-Doughtie
    • 1
  • Katja Hofmann
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamNetherlands

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