A next application prediction service using the BaranC framework

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Hashemi, Mohammad
Herbert, John
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Institute of Electrical and Electronics Engineers (IEEE)
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Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.
Context , Next-App , User model , Dynamic , Satisfaction , Predictive models , Context modeling , Data models , Hidden Markov models , Monitoring , Recommender systems
Hashemi, M. and Herbert, J. (2016) ‘A next application prediction service using the BaranC framework,' IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, GA, 10-14 June. United States: IEEE, pp. 519-523. doi: 10.1109/COMPSAC.2016.30
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