Emine Yilmaz

Understanding & Inferring User Tasks and Needs

Abstract: 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 the traditional methods used for inferring user “intent” and then focus on the recent advancements towards building task-based IR systems and present analytical results which highlight the importance of considering tasks as the focal unit of modeling 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, 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.

Short Bio: Emine Yilmaz is a Turing Fellow and Professor at University College London (UCL), Department of Computer Science, as well as an Amazon Scholar at Amazon Cambridge.  Between 2012 and 2019, she also worked as a research consultant for Microsoft Research Cambridge, where she used to work as a full time researcher prior to joining UCL. Her research until now has received several awards including a Bloomberg Data Science Research Award in 2018, the Karen Sparck Jones Award in 2015 and the Google Faculty Research Award in 2014. Emine’s current research interests include information retrieval, data mining and applications of machine learning. She has published research papers extensively at major venues such as ACM SIGIR, CIKM and WSDM, gave several tutorials as part of top conferences, and organised various workshops. She has served in various roles including PC Chair for ECIR 2019, ACM SIGIR 2018 and ACM ICTIR 2017 Conferences, Practice and Experience Chair for ACM WSDM 2017, and as the Doctoral Consortium Chair for ECIR 2017. She is an elected member of the executive committee of ACM SIGIR.