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    • 4. 发明授权
    • Framework to evaluate content display policies
    • 评估内容显示政策的框架
    • US08504558B2
    • 2013-08-06
    • US12184114
    • 2008-07-31
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • G06F17/30
    • G06Q30/02
    • Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.
    • 使用两种方法评估内容显示策略。 在第一种方法中,使用以“受控”的方式收集关于用户特征和内容特征的信息,生成真实模型。 模拟器会根据真实模型重播用户对门户网页的访问,并模拟与页面上的内容项目的交互。 各种指标用于比较不同的内容项目选择算法。 在第二种方法中,没有建立明确的真理模型。 受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。 衡量总体预测误差的度量用于比较不同的内容项目选择算法。 以受控方式收集的数据在这两种方法中起关键作用。
    • 5. 发明申请
    • FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES
    • 评估内容显示政策的框架
    • US20100030717A1
    • 2010-02-04
    • US12184114
    • 2008-07-31
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • G06N5/02
    • G06Q30/02
    • Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.
    • 使用两种方法评估内容显示策略。 在第一种方法中,使用以“受控”的方式收集关于用户特征和内容特征的信息,生成真实模型。 模拟器会根据真实模型重播用户对门户网页的访问,并模拟与页面上的内容项目的交互。 各种指标用于比较不同的内容项目选择算法。 在第二种方法中,没有建立明确的真理模型。 受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。 衡量总体预测误差的度量用于比较不同的内容项目选择算法。 以受控方式收集的数据在这两种方法中起关键作用。