Representing Tasks with a Graph-Based Method for Supporting Users in Complex Search Tasks

Despite the considerable advancements in modern search systems for assisting users in search tasks of varying types, support for complex tasks that call for multi-round interactions remains challenging. Identifying users' tasks is essential to understanding their evolving information needs and search goals during search sessions to simulate and achieve real-time adaptive search retrievals; thus, it is a crucial research thrust in interactive information retrieval (IIR). While a series of descriptive and formal models have been proposed to characterize complex information search sessions, only a few focus on leveraging dynamic task features in search personalizations to support users in different task stages in an adaptive fashion. This preliminary study presents a heterogeneous graph neural network model for extracting and representing tasks to better understand users' interactive search processes by connecting tasks with search interactions. Our approach's novelty lies in our application of task representation learning, which enables systems to extract hidden task information from users' search behaviors. The results of our evaluative experiments on TREC Session track data highlight the value of our proposed task representation model and illustrate a promising research direction on task-oriented intelligent systems.