Task Intelligence for Search and Recommendation - White, Ryen W.

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Résumé :
While great strides have been made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information and enabling task completion. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, and it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has unlocked new modalities for interacting with information, but these agents will need to be able to work understanding current and future contexts and assist users at task level. This book will focus on task intelligence in the context of search and recommendation. Chapter 1 introduces readers to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). This is followed by presenting several prominent ideas and frameworks about how tasks are conceptualized and represented in Chapter 2. In Chapter 3, the narrative moves to showing how task type relates to user behaviors and search intentions. A task can be explicitly expressed in some cases, such as in a to-do application, but often it is unexpressed. Chapter 4 covers these two scenarios with several related works and case studies. Chapter 5 shows how task knowledge and task models can contribute to addressing emerging retrieval and recommendation problems. Chapter 6 covers evaluation methodologies and metrics for task-based systems, with relevant case studies to demonstrate their uses. Finally, the book concludes in Chapter 7, with ideas for future directions in this important research area.

Biographie:
Chirag Shah is an Associate Professor in the Information School (iSchool) at the University of Washington (UW ) in Seattle. Before UW, he was on the faculty at Rutgers University. His research interests include studies of interactive information retrieval/seeking, trying to understand the task a person is doing and providing proactive recommendations. In addition, he works to make these smart systems also fair, equitable, and transparent under the umbrella of Responsible AI. Dr. Shah received his Ph.D. in Information Science from University of North Carolina (UNC) at Chapel Hill. He directs the InfoSeeking Lab where he investigates issues related to information seeking, human-computer interaction (HCI), and fairness in machine learning. Shah has authored/edited two books on Collaborative Information Seeking (CIS), and a book on Social Information Seeking (SIS). He was a guest editor for the IEEE Computer Special Issue on CIS published in March 2014. He has also written a textbook on Data Science. He has taught undergraduate and graduate courses in IR, HCI, and Data Science at the University of North Carolina (UNC) Chapel Hill, Rutgers University, and University of Washington. He has also taught several courses and tutorials on topics related to search, recommendation, and fairness in machine learning at different international places, including at SIGIR, WSDM, and RecSys conferences, Russian Summer School on Information Retrieval (RuSSIR), and Asian Summer School in Information Access (ASSIA). In 2019, Shah received the Microsoft BCS/BCS IRSG Karen Sparck Jones Award for contributions to information retrieval. He is the Founding Editor-in-Chief of ASIS&T Information Matters.