Mining individualized context-dependent behavioral rules from smartphone data

Due to the demand for many personalized assistance services, the task of mining contextual behavioural rules for individual mobile phone users is the key. Therefore, in this research, we aim to discover individualized behavioural rules based on multi-dimensional contexts (temporal, social, locational contexts). The discovered behavioural rules can be used for building various real-life applications such as smart interruption management, context-aware mobile recommendation, intelligent notification management, to assist individuals in their daily activities. We believe that this research will be beneficial for the society in application point of view, and as such will help raise Swinburne’s research profile.