Jogger: models for context-sensitive reminding

Abstract

We describe research on principles of context-sensitive reminding that show promise for serving in systems that work to jog peoples' memories about information that they may forget. The methods center on the construction and use of a set of distinct probabilistic models that predict (1) items that may be forgotten, (2) the expected relevance of the items in a situation, and (3) the cost of interruption associated with alerting about a reminder. We describe the use of this set of models in the Jogger prototype that employs predictions and decision-theoretic optimization to compute the value of reminders about meetings.

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