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公开(公告)号：US10917425B2
公开(公告)日：2021-02-09
申请号：US16882084
申请日：2020-05-22
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Ziqi Liu , Chaochao Chen , Jun Zhou , Xiaolong Li
IPC: H04L29/06 , G06N20/00 , G06F16/901 , G06F21/55 , G06N5/04
Abstract: Implementations of the present specification disclose graph structure model training and junk account identification methods, apparatuses, and devices. The solution includes: obtaining an account medium network graph, a node in the account medium network graph representing an account and a medium, and at least some edges indicating that a login behavior relationship exists between nodes connected by the edges; obtaining feature data and risk labeling data of the node, the feature data reflecting a login behavior of the corresponding node in a time sequence; and training, based on the account medium network graph, the feature data, and the risk labeling data, a predefined graph structure model for identifying a junk account.
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公开(公告)号：US11551110B2
公开(公告)日：2023-01-10
申请号：US16290208
申请日：2019-03-01
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Jun Zhou
Abstract: A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.
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Patent Agency Ranking
公开(公告)号：US20210248499A1
公开(公告)日：2021-08-12
申请号：US17244811
申请日：2021-04-29
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Liang Li , Jun Zhou
Abstract: A first training participant performs an iterative process until a predetermined condition is satisfied, where the iterative process includes: obtaining, using secret sharing matrix addition and based on the current sub-model of each training participant and a corresponding feature sample subset of each training participant, a current prediction value of the regression model for a feature sample set, where the corresponding feature sample subset of each training participant is obtained by performing vertical segmentation on the feature sample set; determining a prediction difference between the current prediction value and a label corresponding to the current prediction value; sending the prediction difference to each second training participant; and updating a current sub-model of the first training participant based on the current sub-model of the first training participant and a product of a corresponding feature sample subset of the first training participant and the prediction difference.
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公开(公告)号：US11157818B2
公开(公告)日：2021-10-26
申请号：US17158451
申请日：2021-01-26
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Jun Zhou
Abstract: Disclosed are a model training method and apparatus based on gradient boosting decision tree (GBDT). A GBDT algorithm flow is divided into two stages. In the first stage, labeled samples are obtained from a data domain of a service scenario similar to a target service scenario to sequentially train several decision trees, and training residual generated after the training in the first stage is determined; in the second stage, labeled samples are obtained from a data domain of the target service scenario, and several decision trees continue to be trained based on the training residual. Finally, a model applied to the target service scenario is actually obtained by integrating the decision trees trained in the first stage with the decision trees trained in the second stage.
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公开(公告)号：US11386212B2
公开(公告)日：2022-07-12
申请号：US16668945
申请日：2019-10-30
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Liang Li , Chaochao Chen
Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media for secure collaborative computation of a matrix product of a first matrix including private data of a first party and a second matrix including private data of the second party by secret sharing without a trusted initializer. One method includes obtaining a first matrix including private data of the first party; generating a first random matrix; identifying a first sub-matrix and a second sub-matrix of the first random matrix; computing first scrambled private data of the first party based on the first matrix, the first random matrix, the first sub-matrix, and the second sub-matrix; receiving second scrambled private data of the second party; computing a first addend of the matrix product; receiving a second addend of the matrix product; and computing the matrix product by summing the first addend and the second addend.
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公开(公告)号：US11176469B2
公开(公告)日：2021-11-16
申请号：US17244811
申请日：2021-04-29
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Liang Li , Jun Zhou
Abstract: A first training participant performs an iterative process until a predetermined condition is satisfied, where the iterative process includes: obtaining, using secret sharing matrix addition and based on the current sub-model of each training participant and a corresponding feature sample subset of each training participant, a current prediction value of the regression model for a feature sample set, where the corresponding feature sample subset of each training participant is obtained by performing vertical segmentation on the feature sample set; determining a prediction difference between the current prediction value and a label corresponding to the current prediction value; sending the prediction difference to each second training participant; and updating a current sub-model of the first training participant based on the current sub-model of the first training participant and a product of a corresponding feature sample subset of the first training participant and the prediction difference.
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公开(公告)号：US10902332B2
公开(公告)日：2021-01-26
申请号：US16725589
申请日：2019-12-23
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Jun Zhou
Abstract: A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.
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