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Predicting Search Task Difficulty

  • Jaime Arguello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

Search task difficulty refers to a user’s assessment about the amount of effort required to complete a search task. Our goal in this work is to learn predictive models of search task difficulty. We evaluate features derived from the user’s interaction with the search engine as well as features derived from the user’s level of interest in the task and level of prior knowledge in the task domain. In addition to user-interaction features used in prior work, we evaluate features generated from scroll and mouse-movement events on the SERP. In some situations, we may prefer a system that can predict search task difficulty early in the search session. To this end, we evaluate features in terms of whole-session evidence and first-round evidence, which excludes all interactions starting with the second query. Our results found that the most predictive features were different for whole-session vs. first-round prediction, that mouseover features were effective for first-round prediction, and that level of interest and prior knowledge features did not improve performance.

Keywords

Search Task Average Precision Search Behavior Mouse Movement Search Session 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Jaime Arguello
    • 1
  1. 1.University of North Carolina at Chapel HillUSA

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