基于BaranC框架的主动动态预测辅助

Mohammad Hashemi, J. Herbert
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引用次数: 2

摘要

监视用户交互活动为创建可用于预测用户行为和启用用户助理服务的用户模型提供了基础。BaranC框架提供了执行UI监视(并收集所有相关的上下文数据)、构建用户模型和支持使用该用户模型的服务的组件。在本案例研究中,构建Next-App预测服务来演示该框架的使用并评估这种预测服务的有用性。Next-App分析用户的数据,学习模式,为用户建立模型,最后根据用户模型和当前上下文预测用户可能想要使用的应用程序。预测具有前瞻性和动态性;它在响应当前上下文和响应用户模型的变化方面都是动态的,因为随着时间的推移,用户的习惯可能会发生变化。对Next-App的初步评估表明对服务的满意度很高。
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A Pro-active and Dynamic Prediction Assistance Using BaranC Framework
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.
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