Identifying and predicting the states of complex search tasks

Jiqun Liu, Shawon Sarkar, Chirag Shah

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

8 Scopus citations

Abstract

Complex search tasks that involve uncertain solution space and multi-round search iterations are integral to everyday life and information-intensive workplace practices, affecting how people learn, work, and resolve problematic situations. However, current search systems still face plenty of challenges when applied in supporting users engaging in complex search tasks. To address this issue, we seek to explore the dynamic nature of complex search tasks from process-oriented perspective by identifying and predicting implicit task states. Specifically, based upon the Web search logs and user annotation data (regarding information seeking intentions in local search steps, in-situ search problems, and help needed) collected from 132 search sessions in two controlled lab studies, we developed two task state frameworks based on intention state and problem-help state respectively and examined the connection between task states and search behaviors. We report that (1) complex search tasks of different types can be deconstructed and disambiguated based on the associated nonlinear state transition patterns; and (2) the identified task states that cover multiple subtle factors of user cognition can be predicted from search behavioral signals using supervised learning algorithms. This study reveals the way in which complex search tasks are unfolded and manifested in users' search interactions and paves the way for developing state-aware adaptive search supports and system evaluation frameworks.

Original languageEnglish (US)
Title of host publicationCHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages193-202
Number of pages10
ISBN (Electronic)9781450368926
DOIs
StatePublished - Mar 14 2020
Event5th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2020 - Vancouver, Canada
Duration: Mar 14 2020Mar 18 2020

Publication series

NameCHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

Conference

Conference5th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2020
Country/TerritoryCanada
CityVancouver
Period3/14/203/18/20

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Information Systems

Keywords

  • Complex search task
  • Interactive ir
  • Task state

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