Optimizing personalized recommendations with longitudinal data and a future objective
    1.
    发明申请
    Optimizing personalized recommendations with longitudinal data and a future objective 审中-公开
    使用纵向数据和未来目标优化个性化建议

    公开(公告)号:US20150363502A1

    公开(公告)日:2015-12-17

    申请号:US14305607

    申请日:2014-06-16

    Applicant: Google Inc.

    CPC classification number: G06F16/9535

    Abstract: Systems and techniques are provided for optimizing personalized recommendations with longitudinal data and a future objective. An identifier may be received for content items. A user content item history including a list identifying a previously acquired content item may be received. Content item metadata may be received including a correlation between the previously acquired content item and a content item for which an identifier was received, and a correlation between a content item for which an identifier was received and fulfillment of a future objective. A joint probability may be determined for each content item based on the user content item history and the content item metadata, including the probability that the content item will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the content item is acquired by the user.

    Abstract translation: 提供系统和技术,用于通过纵向数据和未来目标优化个性化建议。 可以为内容项目接收标识符。 可以接收包括标识先前获取的内容项的列表的用户内容项历史。 可以接收包括先前获取的内容项目和接收到标识符的内容项目之间的相关性的内容项目元数据,以及接收到标识符的内容项目与未来目标的实现之间的相关性。 可以基于用户内容项目历史和内容项目元数据确定每个内容项目的联合概率,包括用户在被推荐给用户之后将获取内容项目并且将来将满足目标的概率 在用户获取内容项目之后。

    IDENTIFYING PANELISTS BASED ON INPUT INTERACTION PATTERNS
    2.
    发明申请
    IDENTIFYING PANELISTS BASED ON INPUT INTERACTION PATTERNS 有权
    基于输入交互模式识别面板

    公开(公告)号:US20170078257A1

    公开(公告)日:2017-03-16

    申请号:US14851757

    申请日:2015-09-11

    Applicant: Google Inc.

    Inventor: Nicolas Remy

    CPC classification number: H04L63/08 G06F21/316 G06N5/047 G06Q10/00

    Abstract: A panelist identification device for determining an identity of a panelist based on an input interaction pattern of the panelist is provided. Additionally, a method for determining an identity of a panelist based on an input interaction pattern of the panelist is provided. Further, a computer-readable storage device having processor-executable instructions embodied thereon is provided. The instructions are for determining an identity of a panelist based on an input interaction pattern of the panelist.

    Abstract translation: 提供了一种用于基于小组成员的输入交互模式来确定小组成员的身份的小组成员识别装置。 另外,提供了一种基于小组成员的输入交互模式来确定小组成员身份的方法。 此外,提供了具有体现在其上的处理器可执行指令的计算机可读存储设备。 这些说明用于基于小组成员的输入交互模式来确定小组成员的身份。

    Identifying panelists based on input interaction patterns

    公开(公告)号:US09860227B2

    公开(公告)日:2018-01-02

    申请号:US14851757

    申请日:2015-09-11

    Applicant: Google Inc.

    Inventor: Nicolas Remy

    CPC classification number: H04L63/08 G06F21/316 G06N5/047 G06Q10/00

    Abstract: A panelist identification device for determining an identity of a panelist based on an input interaction pattern of the panelist is provided. Additionally, a method for determining an identity of a panelist based on an input interaction pattern of the panelist is provided. Further, a computer-readable storage device having processor-executable instructions embodied thereon is provided. The instructions are for determining an identity of a panelist based on an input interaction pattern of the panelist.

    MEDIA METRICS ESTIMATION FROM LARGE POPULATION DATA
    4.
    发明申请
    MEDIA METRICS ESTIMATION FROM LARGE POPULATION DATA 审中-公开
    大量人口数据的媒体量度估计

    公开(公告)号:US20160165277A1

    公开(公告)日:2016-06-09

    申请号:US13843683

    申请日:2013-03-15

    Applicant: Google Inc.

    CPC classification number: H04N21/251 H04N21/25883 H04N21/25891 H04N21/44222

    Abstract: A method, executed by a processor, for estimating media metrics from large population data includes formatting and storing panel data, the panel data comprising observed viewing data of a plurality of individual panelists and demographic data for the plurality of panelists, the panel being drawn from a large population; accessing the large population data, the large population data comprising household-level viewing data and household level demographics; training a model to estimate viewing audience size based on the observed panel data; estimating, using the trained model, audience size for each household in the large population data; estimating a viewing score for each individual viewer in a plurality of households in the large population data; and combining the estimates of audience size and viewing score to produce probabilities that each of the viewers in the household viewed a specific media event.

    Abstract translation: 由处理器执行的用于从大量数据估计媒体度量的方法包括格式化和存储面板数据,面板数据包括多个单独小组成员的观察数据和多个小组成员的人口统计数据,面板从 人口众多 获取大量人口数据,大量人口数据包括家庭层面的观看数据和家庭层面的人口统计; 培训模型以根据观察到的面板数据估计观众人数; 在大量人口数据中,使用受过训练的模型估计每个家庭的受众人数; 估计大群体数据中多个家庭中每个单独观众的观看分数; 并且将观众大小和观看分数的估计结合起来,以产生每个家庭观众观看特定媒体事件的概率。

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