Search Engines that Learn from Implicit Feedback

  • Authors:
  • Thorsten Joachims;Filip Radlinski

  • Affiliations:
  • Cornell University;Cornell University

  • Venue:
  • Computer
  • Year:
  • 2007

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Abstract

Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection.