SELECTING ONE OR MORE COMPONENTS TO BE INCLUDED IN A CONTENT ITEM OPTIMIZED FOR AN ONLINE SYSTEM USER

    公开(公告)号:US20180121953A1

    公开(公告)日:2018-05-03

    申请号:US15340855

    申请日:2016-11-01

    Applicant: Facebook, Inc.

    CPC classification number: G06Q30/0254 G06N5/003 G06N20/00 G06Q30/0276

    Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

    Selecting one or more components to be included in a content item optimized for an online system user

    公开(公告)号:US11232482B2

    公开(公告)日:2022-01-25

    申请号:US15340855

    申请日:2016-11-01

    Applicant: Facebook, Inc.

    Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

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