SEGMENTATION TECHNIQUES FOR LEARNING USER PATTERNS TO SUGGEST APPLICATIONS RESPONSIVE TO AN EVENT ON A DEVICE
    16.
    发明申请
    SEGMENTATION TECHNIQUES FOR LEARNING USER PATTERNS TO SUGGEST APPLICATIONS RESPONSIVE TO AN EVENT ON A DEVICE 审中-公开
    用户模式学习应用程序对于设备上的事件的分类技术

    公开(公告)号:US20160357774A1

    公开(公告)日:2016-12-08

    申请号:US14732287

    申请日:2015-06-05

    Applicant: Apple Inc.

    CPC classification number: G06F17/18

    Abstract: Systems, methods, and apparatuses are provided for suggesting one or more applications to a user based on an event. A prediction model can correspond to a particular event. The suggested application can be determined using one or more properties of the computing device. For example, a particular sub-model can be generated from a subset of historical data that are about user interactions after occurrences of the event and that are gathered when the device has the one or more properties. A tree of sub-models may be determined corresponding to different contexts of properties of the computing device. And, various criteria can be used to determine when to generate a sub-model, e.g., a confidence level in the sub-model providing a correct prediction in the subset of historical data and an information gain (entropy decrease) in the distribution of the historical data relative to a parent model.

    Abstract translation: 提供系统,方法和装置,用于基于事件向用户建议一个或多个应用程序。 预测模型可以对应于特定事件。 可以使用计算设备的一个或多个属性来确定建议的应用。 例如,可以从事件发生之后的用户交互的历史数据的子集生成特定子模型,并且当设备具有一个或多个属性时收集该子模型。 可以根据计算设备的不同属性上下文确定子模型树。 并且,可以使用各种标准来确定什么时候生成子模型,例如在子模型中的置信水平,在历史数据的子集中提供正确的预测,并且在分布中的信息增益(熵减小) 相对于父模型的历史数据。

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