Interest has been growing in opportunities to build and deploy
\nstatistical models that can infer a computer user\u2019s current
\ninterruptability from computer activity and relevant contextual
\ninformation. We describe a system that intermittently asks users to
\nassess their perceived interruptability during a training phase and
\nthat builds decision-theoretic models with the ability to predict the
\ncost of interrupting the user. The models are used at run-time to
\ncompute the expected cost of interruptions, providing a mediator
\nfor incoming notifications, based on a consideration of a user\u2019s
\ncurrent and recent history of computer activity, meeting status,
\nlocation, time of day, and whether a conversation is detected.<\/p>\n","protected":false},"excerpt":{"rendered":"
Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user\u2019s current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decision-theoretic models with the ability to predict the 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