Automated targeting of content components
    1.
    发明授权
    Automated targeting of content components 有权
    自动定位内容组件

    公开(公告)号:US08799814B1

    公开(公告)日:2014-08-05

    申请号:US12035618

    申请日:2008-02-22

    CPC classification number: G06Q30/0242 G06Q30/0201

    Abstract: Techniques for automated targeting of content components to users are described. Content components are selected and exposed through renderable pages for viewing by a population of users. User activity following exposure is tracked in an effort to identify which types of users (as characterized by certain attributes) are likely to act on the content components. The users are segmented into groups according to the attributes and the segments are fed back to aid in selection of content components to be exposed to the users. This enables more granular targeting of the content components to those users who exhibit the attributes that define the specific groups.

    Abstract translation: 描述了内容组件自动定位到用户的技术。 通过可渲染的页面来选择并公开内容组件,以供用户群体观看。 跟踪曝光后的用户活动,以确定哪些类型的用户(由某些属性表征)可能对内容组件起作用。 用户根据属性将它们分组成一组,并且反馈片段以帮助选择要暴露给用户的内容组件。 这样可以将内容组件更精细地定位到展示定义特定组的属性的用户。

    Predicting geographic location associated with network address
    7.
    发明授权
    Predicting geographic location associated with network address 有权
    预测与网络地址相关的地理位置

    公开(公告)号:US07937336B1

    公开(公告)日:2011-05-03

    申请号:US11771679

    申请日:2007-06-29

    CPC classification number: H04L61/6095 H04L29/1299 H04L67/18

    Abstract: A decision tree is provided as a machine learning classifier to predict a user attribute, such as a geographical location of a user, based on a network address. More specifically, the decision tree is constructed via machine learning on a set of sample data that reflects a relationship between a network address and a user attribute of a “known user” whose profile information is recognizable. For a given network address, the decision tree can be used as a machine learning classifier to predict the most likely user attribute of a potential user. With the predicted attribute, a network service can target a group of potential users for various campaigns without recognizing the identities of the potential users.

    Abstract translation: 提供决策树作为机器学习分类器,以基于网络地址来预测用户属性,诸如用户的地理位置。 更具体地说,通过机器学习对一组样本数据构成决策树,该集合的样本数据反映了网络地址与其可识别资料信息的“已知用户”的用户属性之间的关系。 对于给定的网络地址,决策树可以用作机器学习分类器来预测潜在用户的最可能的用户属性。 利用预测属性,网络服务可以针对一组潜在用户进行各种活动,而不会识别潜在用户的身份。

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