A pro-active and dynamic prediction assistance using BaranC framework
Association for Computing Machinery
Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service 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; it is dynamic both in responding to the current context, and also 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.
Forecasting , Software engineering , App predictions , Context data , Dynamic prediction , Level of satisfaction , User assistants , User behaviour , User interaction , User modeling
Hashemi, M. and Herbert, J. (2016) ‘A pro-active and dynamic prediction assistance using BaranC framework’, Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16), Austin, Texas, 14-22 May. New York, USA: ACM, pp. 269-270. doi: 10.1145/2897073.2897759
© 2016, the Authors. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16) http://doi.acm.org/10.1145/2897073.2897759