A next application prediction service using the BaranC framework

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Date
2016-08-25
Authors
Hashemi, Mohammad
Herbert, John
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Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
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.
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Keywords
Context , Next-App , User model , Dynamic , Satisfaction , Predictive models , Context modeling , Data models , Hidden Markov models , Monitoring , Recommender systems
Citation
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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© 2016, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.