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    Predicting latent metrics about user interactions with content based on combination of predicted user interactions with the content
    1.
    发明授权
    Predicting latent metrics about user interactions with content based on combination of predicted user interactions with the content 有权

    公开(公告)号：US11094021B2

    公开(公告)日：2021-08-17

    申请号：US15174865

    申请日：2016-06-06

    Applicant: Facebook, Inc.

    Inventor： Robert Oliver Burns Zeldin ,  Nathan John Davis ,  Anand Sumatilal Bhalgat ,  Harsh Doshi ,  Hao Song

    IPC: G06Q50/00 ,  G06Q30/08

    Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

    Measuring Advertisement Lift
    3.
    发明申请
    Measuring Advertisement Lift 审中-公开
    测量广告提升

    公开(公告)号：US20170068987A1

    公开(公告)日：2017-03-09

    申请号：US14847070

    申请日：2015-09-08

    Applicant: Facebook, Inc.

    Inventor： Michael Daniel Levinson ,  William Bullock ,  Nathan John Davis ,  Chinmay Deepak Karande

    IPC: G06Q30/02

    CPC classification number: G06Q30/0246 ,  G06Q30/0275

    Abstract: An advertisement system measures an ad lift metric for advertisement campaigns, which indicates the increase in conversions that can be attributed to the advertisement campaign. As impression opportunities become available for users for the ad in the lift study, the advertisement system determines whether the user is in a test group or a control group. To limit bias in the lift study, rather than holding out ads from being provided to users after the ad has been selected for the user and right before the impression, the system holds out the ads at a higher level in the ad selection process. In this manner, not all test group users receive the advertisement. The system computes the lift metric as e.g., the incremental lift (difference between conversion rates in the test and control groups), and this is divided by conversion rate of an exposed target group minus the incremental lift.

    Abstract translation: 广告系统衡量广告活动的广告提升度量，这表明可以归因于广告活动的转化次数增加。 由于电梯研究中的广告的用户可以看到展示机会，所以广告系统确定用户是否在测试组或对照组中。 为了限制电梯研究中的偏见，而不是在为用户选择广告并在展示之前提供给用户，而是在广告选择过程中将广告提升到更高级别。 以这种方式，并非所有测试组用户都收到广告。 系统计算升力量度，例如增量提升（测试和控制组中转换率之间的差异），并将其除以暴露目标组的转化率减去增量提升。

    Systems and methods for determining television consumption behavior
    4.
    发明授权
    Systems and methods for determining television consumption behavior 有权

    公开(公告)号：US10425687B1

    公开(公告)日：2019-09-24

    申请号：US15728792

    申请日：2017-10-10

    Applicant: Facebook, Inc.

    Inventor： Timon Arya Karnezos ,  Nathan John Davis

    IPC: H04N21/442 ,  H04N21/466 ,  H04N21/422 ,  H04N21/4223 ,  G06K9/00 ,  H04N21/81

    Abstract: In one embodiment, a method includes determining television content that a particular user is currently watching on a television and determining, using one or more sensors, an attention level for the particular user. The attention level indicates an amount of attention paid by the particular user to the television content. The method further includes generating an attention profile for the television content by aggregating the particular user's attention level for the television content with stored information associated with a plurality of other users about the television content. The attention profile indicates a number of users who paid attention to the television content. The method further includes determining digital content available on a social-networking system that is related to the television content and providing a comparison of the number of users who paid attention to the television content with engagement on the social-networking system with the related digital content.

    PREDICTING LATENT METRICS ABOUT USER INTERACTIONS WITH CONTENT BASED ON COMBINATION OF PREDICTED USER INTERACTIONS WITH THE CONTENT
    7.
    发明申请
    PREDICTING LATENT METRICS ABOUT USER INTERACTIONS WITH CONTENT BASED ON COMBINATION OF PREDICTED USER INTERACTIONS WITH THE CONTENT 审中-公开

    公开(公告)号：US20170352109A1

    公开(公告)日：2017-12-07

    申请号：US15174865

    申请日：2016-06-06

    Applicant: Facebook, Inc.

    Inventor： Robert Oliver Burns Zeldin ,  Nathan John Davis ,  Anand Sumatilal Bhalgat ,  Harsh Doshi ,  Hao Song

    IPC: G06Q50/00 ,  G06Q30/08

    CPC classification number: G06Q50/01 ,  G06Q30/08

    Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

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