A context and user aware smart notification system

Nowadays, notifications are increasingly gaining momentum in our society. New smart devices and appliances are developed everyday with the ability to generate, send and show messages about their status, acquired data and/or information received from other devices and users. Consequently, the number of notifications received by a user is growing and the tolerance to them could decrease in a short time. This paper presents a smart notification system that uses machine learning algorithms to adequately manage incoming notifications. According to context awareness and user habits, the system decides: (a) who should receive an incoming notification; (b) what is the best moment to show the notification to the chosen user(s); (c) on which device(s) the chosen user(s) should receive the notification; (d) which is the best way to notify the incoming notification. After the design of a general architecture, as a first step in building such a system, three different machine learning algorithms were compared in the task of establishing the best device on which the incoming notification should be delivered. The algorithms were applied to a dataset derived from real data provided by the MIT Media Laboratory Reality Mining project, enriched with additional synthetic information.