Despite to the vast advances in capacity and capability of
computers over the past two decades, the representation of users,
their tasks, activities and preferences in application software
has changed little since the dawn of modern personal computing
and personal information management (PIM) tools in the 1980's.
This has left applications with a deficiency of appropriate
information about users, which, when compared to the quantity and
diversity of information managed for users by applications today,
has created a fundamental bottleneck with respect to how well
modern application software can serve usersù tasks and
informational needs.
In this project, we propose a strategy for enriching end-user
applications with information about their users obtained using
three simple strategies: mining information already available on
a userùs own personal devices, logging user activity and
contexts unobtrusively through these same devices, and by opening
up channels by which users can easily and flexibly express
knowledge to the system as part of his or her workflow. Using
this information, we demonstrate ways by which systems can
provide cognitive, interaction, automation and task -oriented
support to users that can save users time, effort, and
frustration. Finally, we demonstrate how client-side user
modeling approaches, unlike centralized collaborative approaches
prevalent today, can scale to employ a vast quantity of private,
sensitive information without sacrificing user privacy.
