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    User type affinity estimation using gamma-poisson model
    2.
    发明授权
    User type affinity estimation using gamma-poisson model 有权

    公开(公告)号：US11907312B1

    公开(公告)日：2024-02-20

    申请号：US15862403

    申请日：2018-01-04

    Applicant: Snap Inc.

    Inventor： Yanen Li ,  Fei Wu ,  Ning Xu

    IPC: G06F16/9535 ,  G06N20/00 ,  G06N7/00 ,  H04L67/50 ,  G06Q50/00

    CPC classification number: G06F16/9535 ,  G06N7/00 ,  G06N20/00 ,  H04L67/535 ,  G06Q50/01

    Abstract: Systems and methods are provided for generating a user click history table and a random bucket training table, generating training data for training a user-type-affinity machine learning model by combining the user click history table and the random bucket training table, and training the user-type-affinity machine learning model with the generated training data. The systems and methods further provide for generating a user click prediction table and generating user-type-affinity prediction values for each of the plurality of users by inputting the user click prediction table into the user-type-affinity machine learning model.

    Data retrieval using reinforced co-learning for semi-supervised ranking
    3.
    发明授权
    Data retrieval using reinforced co-learning for semi-supervised ranking 有权

    公开(公告)号：US11544553B1

    公开(公告)日：2023-01-03

    申请号：US16448749

    申请日：2019-06-21

    Applicant: Snap Inc.

    Inventor： Shibi He ,  Yanen Li ,  Ning Xu

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.

    DATA RETRIEVAL USING REINFORCED CO-LEARNING FOR SEMI-SUPERVISED RANKING
    4.
    发明申请
    DATA RETRIEVAL USING REINFORCED CO-LEARNING FOR SEMI-SUPERVISED RANKING 有权

    公开(公告)号：US20230053009A1

    公开(公告)日：2023-02-16

    申请号：US17972459

    申请日：2022-10-24

    Applicant: Snap Inc.

    Inventor： Shibi He ,  Yanen Li ,  Ning Xu

    IPC: G06N3/08 ,  G06K9/62 ,  G06N3/04

    Abstract: A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.

    MULTIMODAL MACHINE LEARNING SELECTOR
    9.
    发明申请
    MULTIMODAL MACHINE LEARNING SELECTOR 审中-公开

    公开(公告)号：US20190236450A1

    公开(公告)日：2019-08-01

    申请号：US16230909

    申请日：2018-12-21

    Applicant: Snap Inc.

    Inventor： Yanen Li ,  Kuan Liu ,  Ning Xu

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: Multimodal data sets of a given entity (e.g., a user) can be processed using a plurality of different machine learning schemes, such as a recurrent neural network and a fully connected neural network. Representations generated by the networks can be combined in an additive layer and further in a multiplicative layer that emphasizes informative modalities and tolerates less informative modalities.

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