Machine learning modeling using social graph signals

    公开(公告)号:US11966853B2

    公开(公告)日:2024-04-23

    申请号:US17225767

    申请日:2021-04-08

    Applicant: Snap Inc.

    CPC classification number: G06N5/022 G06F16/951 G06N20/00

    Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

    MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS

    公开(公告)号:US20240202550A1

    公开(公告)日:2024-06-20

    申请号:US18592154

    申请日:2024-02-29

    Applicant: Snap Inc.

    CPC classification number: G06N5/022 G06F16/951 G06N20/00

    Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

    Machine learning modeling using social graph signals

    公开(公告)号:US11003997B1

    公开(公告)日:2021-05-11

    申请号:US15725075

    申请日:2017-10-04

    Applicant: Snap Inc.

    Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

    MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS

    公开(公告)号:US20210224661A1

    公开(公告)日:2021-07-22

    申请号:US17225767

    申请日:2021-04-08

    Applicant: Snap Inc.

    Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

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