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Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users.
In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. This book presents an overview of the current state of the art in the area of intelligent notification mechanisms that rely on the awareness of users' context and preferences. We first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.
List of contents
Preface.- Acknowledgments.- Introduction.- Interruptions.- Cost of Interruption.- An Overview of Interruptibility Management.- Interruptibility Management in Desktop Environments.- Interruptibility Management in Mobile Environments.- Limitations of the State of the Art and Open Challenges.- Summary.- Bibliography.- Authors' Biographies.
About the author
Abhinav Mehrotra is a Machine Learning Engineer at Samsung AI Center, Cambridge, UK. He obtained his Ph.D. in Computer Science from the University of Birmingham, UK, where he worked on intelligent mobile notification systems. He joined University College London (UCL) as a postdoctoral researcher, where his research focused on behavior modeling and digital health through the analysis of contextual information obtained via embedded sensors. After his postdoctoral work at UCL, he joined Samsung AI Center, where his research efforts are toward optimization of machine learning models in order to support on-device AI systems, and design of intelligent speech interaction systems.Mirco Musolesi is Full Professor of Data Science at University College London (UCL) and a Turing Fellow at the Alan Turing Institute. He is also Full Professor of Computer Science at the University of Bologna. At UCL he leads the Intelligent Social Systems Lab. He received a Ph.D. in Computer Science from UCL and a Masters in Electronic Engineering from the University of Bologna. After postdoctoral work at Dartmouth College and Cambridge, he held academic posts at St Andrews and Birmingham. Over the past years, the focus of the work of his lab has been the design of next-generation intelligent systems mainly based on computational and mathematical models of human behavior and social dynamics. More recently, he has been interested in designing autonomous systems, possibly with humans in the loop. He is interested in both theoretical and systems-oriented aspects of these research areas. He works at the interface of several disciplines including Ubiquitous Systems, Autonomous Systems, Machine Learning, Artificial Intelligence, and Computational Social Science
