Julie S. Weber

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We present a new algorithm for active learning embedded within an interactive calendar management system that learns its users' scheduling preferences. When the system receives a meeting request, the active learner selects a set of alternative solutions to present to the user; learning is then achieved by noting the user's preferences for the selected(More)
We are developing an adaptive reminding system that tailors its reminders to its users' reminding preferences through real-time interaction and feedback. To determine the potential utility of such a system, we conducted a multi-phase user study, presented in this paper, in which we evaluate people's preferences for the visual presentation of reminders.(More)
We describe a simulation system that models the user of a calendar-management tool. The tool is intended to learn the user's scheduling preferences, and we employ the simulator to evaluate learning strategies. The simulated user is instantiated with a set of preferences over local and global features of a schedule such as the level of importance of a(More)
We studied people's perception of and response to a set of visual and auditory notifications issued in a multi-task environment. Primary findings show that participants' reactive preference ratings of notifications delivered in various contexts during experimentation appear to contradict their reflective, overall ratings of the notification formats when(More)
This extended abstract describes ongoing work in the development of an intelligent assistant that interacts with its user in a personalized fashion, deciding whether, when and how to interact based on a user's needs and preferences. I consider two types of users: people who work in an office environment and require assistance with managing their daily(More)
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