Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

  • Authors:
  • Thorsten Joachims;Laura Granka;Bing Pan;Helene Hembrooke;Filip Radlinski;Geri Gay

  • Affiliations:
  • Cornell University, Ithaca, NY;Google Inc., Mountain View, CA;College of Charleston, Charleston, SC;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2007

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Abstract

This article examines the reliability of implicit feedback generated from clickthrough data and query reformulations in World Wide Web (WWW) search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. We find that such relative preferences are accurate not only between results from an individual query, but across multiple sets of results within chains of query reformulations.