Exploring and predicting search task difficulty

  • Authors:

  • Jingjing Liu;Chang Liu;Michael Cole;Nicholas J. Belkin;Xiangmin Zhang

  • Affiliations:

  • University of South Carolina, Columbia, SC, USA;Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunwick, NJ, USA;Wayne State University, Detroit, MI, USA

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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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.