Exploration or Fact-Finding: Inferring User's Search Activity Just in Time

Being able to differentiate between search activities a user is currently engaged in is crucial for adaptive information retrieval systems in order to provide appropriate support to the user in time. In this paper, we conduct an investigation into modeling two kinds of search activities: exploratory activities and fact-finding activities during web search. Specifically, we consider the case where a user is conducting consecutive fact-finding searches on multiple topics. This information behavior is also known as multitasking search. The goal of our research is to build models of users' web information-seeking behavior in order to differentiate between search activities caused by exploratory search tasks and consecutive fact-finding search tasks while users are still searching. In order to build search process models, we have designed and conducted a user study where the participants interact with a common web search engine. Based on the gathered log data, we built search models based on (Hidden) Markov Models and analyze the results. Our results yield to classification rates between 73.6% and 92.1% using different Markov Models and different data configurations. Furthermore, even with interaction sequences of a limited length, the models reach a classification rate of 85.6% within the first four interactions and about 89% with 30 and more interactions. Using a reduced data set the models still reach an accuracy of 87.7%. That is, within a single session the trained models can be used to detect the search activity to provide appropriate user support.