A key dimension in personalization of converged (wireless and wireline, web) communication services is adapting each service to a user's context, and thus tailoring the services to the daily lives of individual users. The Intuitive Network Application (INA) framework being developed at Bell Labs uses both machine learning techniques as well as user feedback to determine a user's profile and preferences. This paper explores how this information can then be used by the network to automatically infer a user's context and to tailor the service behavior to the needs of the user in that context.
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