Exploring and predicting search task difficulty

Jingjing Liu, Chang Liu, Michael Cole, Nicholas Belkin, Xiangmin Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

We report on an investigation of behavioral differences between users in difficult and easy search tasks. Behavioral factors that can be used in real-time to predict task difficulty are identified. User data was collected in a controlled lab experiment (n=38) where each participant completed four search tasks in the genomics domain. We looked at user behaviors that can be obtained by systems at three levels, distinguished by the time point when the measurements can be done. They are: 1) first-round level at the beginning of the search, 2) accumulated level during the search, and 3) whole-session level by the end of the search. Results show that a number of user behaviors at all three levels differed between easy and difficult tasks. Models predicting task difficulty at all three levels were developed and evaluated. A real-time model incorporating first-round and accumulated levels of behaviors (FA) had fairly good prediction performance (accuracy 83%; precision 88%), which is comparable with the model using the whole-session level behaviors which are not real-time (accuracy 75%; precision 92%). We also found that for efficiency purpose, using only a limited number of significant variables (FC-FA) can obtain a prediction accuracy of 75%, with a precision of 88%. Our findings can help search systems predict task difficulty and adapt search results to users.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages1313-1322
Number of pages10
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • accumulated level
  • difficulty prediction
  • first-round level
  • task difficulty
  • user behavior
  • user modeling
  • whole-session level

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