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Establishing and utilizing behavioral data thresholds for deep learning and other models to identify users across digital space

机译:建立和利用行为数据阈值进行深度学习和其他模型,以跨数字空间识别用户

摘要

The present disclosure relates to systems, methods, and non-transitory computer readable media for providing customized digital content to a client device of a target user by generating and utilizing an event-number-specific user classification model. For example, the disclosed systems can train a user classification model based on a set of minimum prior event users who each satisfy a particular event number threshold. The event number threshold can correspond to a number of events that, when implemented by the target user identification system, cause the user classification model to converge. Upon training, the disclosed systems can detect an event associated with the client device of the target user, utilize the trained event-number-specific user classification model to identify the client device corresponding to the target user, and provide customized digital content to the client device of the target user.
机译:本公开涉及用于通过生成和利用特定于事件编号的用户分类模型来向目标用户的客户端设备提供定制的数字内容的系统,方法和非暂时性计算机可读介质。例如,所公开的系统可以基于分别满足特定事件数量阈值的一组最小先验事件用户来训练用户分类模型。事件数量阈值可以对应于由目标用户标识系统实施时导致用户分类模型收敛的事件数量。在训练后,所公开的系统可以检测与目标用户的客户端设备相关联的事件,利用训练后的特定于事件编号的用户分类模型来识别与目标用户相对应的客户端设备,并向客户端提供定制的数字内容目标用户的设备。

著录项

  • 公开/公告号US10511681B2

    专利类型

  • 公开/公告日2019-12-17

    原文格式PDF

  • 申请/专利权人 ADOBE INC.;

    申请/专利号US201815910679

  • 发明设计人 KOUROSH MODARRESI;

    申请日2018-03-02

  • 分类号H04L29/08;G06N3/08;G06N3/04;G06N20;H04L29/06;

  • 国家 US

  • 入库时间 2022-08-21 11:29:00

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