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A Methodology for Evaluating Aggregated Search Results

  • Jaime Arguello
  • Fernando Diaz
  • Jamie Callan
  • Ben Carterette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)

Abstract

Aggregated search is the task of incorporating results from different specialized search services, or verticals, into Web search results. While most prior work focuses on deciding which verticals to present, the task of deciding where in the Web results to embed the vertical results has received less attention. We propose a methodology for evaluating an aggregated set of results. Our method elicits a relatively small number of human judgements for a given query and then uses these to facilitate a metric-based evaluation of any possible presentation for the query. An extensive user study with 13 verticals confirms that, when users prefer one presentation of results over another, our metric agrees with the stated preference. By using Amazon’s Mechanical Turk, we show that reliable assessments can be obtained quickly and inexpensively.

Keywords

Discordant Pair Preference Judgement Majority Preference Vertical Block Commercial Search Engine 
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 2011

Authors and Affiliations

  • Jaime Arguello
    • 1
  • Fernando Diaz
    • 2
  • Jamie Callan
    • 1
  • Ben Carterette
    • 3
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.Yahoo! ResearchUSA
  3. 3.University of DelawareUSA

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