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People rely on task management applications and digital assistants to capture and track their tasks, and help with executing them. The burden of organizing and scheduling time for tasks continues to reside with users of these systems, despite the high cognitive load associated with these activities. Users stand to benefit greatly from a task management system capable of prioritizing their pending tasks, thus saving them time and effort. In this article, we make three main contributions. First, we propose the problem of task prioritization, formulating it as a ranking over a user’s pending tasks given a history of previous interactions with a task management system. Second, we perform an extensive analysis on the large-scale anonymized, de-identified logs of a popular task management application, deriving a dataset of grounded, real-world tasks from which to learn and evaluate our proposed system. We also identify patterns in how people record tasks as complete, which vary consistently with the nature of the task. Third, we propose a novel contextual deep learning solution capable of performing personalized task prioritization. In a battery of tests, we show that this approach outperforms several operational baselines and other sequential ranking models from previous work. Our findings have implications for understanding the ways people prioritize and manage tasks with digital tools, and in the design of support for users of task management applications. [ABSTRACT FROM AUTHOR]

Information systems, such as task management applications and digital assistants, can help people keep track of tasks of different types and different time durations, ranging from a few minutes to days or weeks. Helping people better manage their tasks and their time are core capabilities of assistive technologies, situated within a broader context of supporting more effective information access and use. Throughout the course of a day, there are typically many short time periods of downtime (e.g., five minutes or less) available to individuals. Microtasks are simple tasks that can be tackled in such short amounts of time. Identifying microtasks in task lists could help people utilize these periods of low activity to make progress on their task backlog. We define actionable tasks as self-contained tasks that need to be completed or acted on. However, not all to-do tasks are actionable. Many task lists are collections of miscellaneous items that can be completed at any time (e.g., books to read, movies to watch), notes (e.g., names, addresses), or the individual items are constituents in a list that is itself a task (e.g., a grocery list). In this article, we introduce the novel challenge of microtask detection, and we present machine-learned models for automatically determining which tasks are actionable and which of these actionable tasks aremicrotasks. Experiments show that our models can accurately identify actionable tasks, accurately detect actionable microtasks, and that we can combine these models to generate a solution that scales microtask detection to all tasks. We discuss our findings in detail, along with their limitations. These findings have implications for the design of systems to help people make the most of their time. [ABSTRACT FROM AUTHOR]

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice. • This paper aims to better understand the need for Digital Assistants (DAs) to support daily task management in work-related settings. • Demonstrating the perceived utility of Digital Assistants that support user time, tasks, and information by target users. • Different occupation roles require varied support domains from Digital Assistant • Task characteristics affect the Digital Assistant's features that users desire. [ABSTRACT FROM AUTHOR]