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Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs

Filip Radlinski,T. Joachims

Published 2006 in AAAI Conference on Artificial Intelligence

ABSTRACT

Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a user clicking on a result is strongly biased toward documents presented higher in the result set irrespective of relevance. We introduce a simple method to modify the presentation of search results that provably gives relevance judgments that are unaffected by presentation bias under reasonable assumptions. We validate this property of the training data in interactive real world experiments. Finally, we show that using these unbiased relevance judgments learning methods can be guaranteed to converge to an ideal ranking given sufficient data.

PUBLICATION RECORD

  • Publication year

    2006

  • Venue

    AAAI Conference on Artificial Intelligence

  • Publication date

    2006-05-08

  • Fields of study

    Computer Science

  • Identifiers
    arXiv cs/0605037
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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