Understanding & Inferring User Tasks and Needs
- Rishabh Mehrotra
- Emine Yilmaz
- Ahmed Hassan Awadallah
Search behavior, and information behavior more generally, is often motivated by tasks that prompt search processes that are often lengthy, iterative, and intermittent, and are characterized by distinct stages, shifting goals and multitasking. Current search systems do not provide adequate support for users tackling complex tasks due to which the cognitive burden of keeping track of such tasks is placed on the searcher.
Developing a comprehensive understanding of user’s tasks would help in providing better support and recommendations to users based on their contextual information and as a result, help users accomplish the task. In this tutorial, we begin by discussing recent advancements towards building task based IR systems and present analytical results which highlight the importance of considering tasks as the focal unit of modelling search behavior.
Additionally, we consider the challenge of extracting tasks from a given collection of search log data and present some recently proposed task extraction techniques which rely on recent advancements in bayesian non parametrics, word embeddings, structured predictions and deep learning. We go beyond traditional web search scenarios, and characterize user tasks with conversational agents and digital assistants, including the recently introduced voice only assistants.
We additionally present a detailed overview of task based evaluation techniques. Finally, we present applications of task inference techniques alongside discussing the implications of task based systems & summarize few key open research questions.