Flexible multi-task neutral network for content ranking

    公开(公告)号:US11017287B2

    公开(公告)日:2021-05-25

    申请号:US15784002

    申请日:2017-10-13

    Applicant: Facebook, Inc.

    Inventor: Liang Xiong Yan Zhu

    Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of independent layers, a plurality of shared layers and a plurality of separate layers. Each independent layer is configured to extract features, for each task, that are not shared across the plurality of tasks. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the features extracted from the plurality of independent layers and the plurality of shared layers.

    FLEXIBLE MULTI-TASK NEUTRAL NETWORK FOR CONTENT RANKING

    公开(公告)号:US20190114528A1

    公开(公告)日:2019-04-18

    申请号:US15784002

    申请日:2017-10-13

    Applicant: Facebook, Inc.

    Inventor: Liang Xiong Yan Zhu

    Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of independent layers, a plurality of shared layers and a plurality of separate layers. Each independent layer is configured to extract features, for each task, that are not shared across the plurality of tasks. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the features extracted from the plurality of independent layers and the plurality of shared layers.

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