Extracting the Implicit Search States from Explicit Behavioral Signals in Complex Search Tasks
Ben Wang, Jiqun Liu · Proceedings of the Association for Information Science and Technology · 2021
Abstract In a complex search task, users often go through different search states during search interactions under complex search tasks. Automatically identify these search states from observable behavioral measures will enhance our understanding of users' cognitive variations and serve as the basis for state‐aware adaptive search recommendations and evaluations. To achieve this goal, this state seeks to extract implicit search states from explicit behavioral signals and explore the possible connection between automatically clustered search states and the existing state typologies developed based on user labeling and expert annotations. The results from our preliminary work indicate that search states and the associated behavioral patterns can be extracted through clustering analysis in multiple datasets, and that the identified clusters/states can be mapped to the states identified via qualitative coding. Our study demonstrates the feasibility of automatically extracting search states and pave the path towards state‐aware adaptive search systems.