| 摘要 |
Model enhancement architecture that provides selective sampling of data to enhance model performance where model testing is deemed to be poor. Sampling can include direct interaction with the user while the user is logged-in to the computing system. The system can be used to infer a computer user's current interruptability based on computer activity and relevant contextual information. Personalized models can then be created that are utilized to determine a cost of interruption and an expected utility. A modeling component is provided that builds and runs models based on data. The data can be any type of data such as application data, user profile data, tracking data, user state data, user situation data, and so on. A sampling component samples the data based on failure analysis of the model. The architecture is a utility-centric approach to gathering data to maximally enhance the current model.
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