公开(公告)号：US20240020541A1

公开(公告)日：2024-01-18

申请号：US18476830

申请日：2023-09-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Wei GUO ,  Huifeng GUO ,  Yong GAO ,  Ruiming TANG ,  Wenzhi LIU ,  Xiuqiang HE

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: This application describes a model training method, applied to the field of artificial intelligence. The method includes a computing core of a first processor obtains an embedding used for model training, and writes an updated embedding to a first memory of the first processor instead of transferring the updated embedding to a second processor after model training is completed. In this application, after updating an embedding, the first processor saves the updated embedding to the first memory of the first processor. Without needing to wait for the second processor to complete a process of transferring a second target embedding to a GPU, the first processor may directly obtain the updated embedding and perform model training of a next round based on the updated embedding, provided that the first processor may obtain a latest updated embedding.

公开(公告)号：US20250156765A1

公开(公告)日：2025-05-15

申请号：US19019926

申请日：2025-01-14

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Quanyu DAI ,  Peng WU ,  Haoxuan LI ,  Zhenhua DONG ,  Ruiming TANG

IPC: G06N20/00

Abstract: Embodiments of this application disclose a model training method and a related apparatus, to improve a generalization capability of a prediction model. The method in embodiments of this application includes: calculating a loss function of an error imputation model based on a first error of a prediction result of a prediction model for first sample data, a first output of the error imputation model, and a probability that the first sample data is observed; and then updating a parameter of the error imputation model based on the loss function of the error imputation model, where the first output of the error imputation model represents a predicted value of the first error, the loss function of the error imputation model includes a bias term and a variance term.

公开(公告)号：US20250095047A1

公开(公告)日：2025-03-20

申请号：US18968747

申请日：2024-12-04

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Qinglin JIA ,  Jieming ZHU ,  Guohao CAI ,  Ruiming TANG ,  Zhenhua DONG

IPC: G06Q30/0601

Abstract: This application discloses an item recommendation method and a related device thereof, so that a probability of tapping an item by the user can be accurately predicted, to improve overall prediction precision of a model. The method in this application includes obtaining first information, where the first information includes attribute information of a user and attribute information of an item. The method also include processing the first information by using a first model to obtain a first processing result, where the first processing result is used to determine the item recommended to the user. Furthermore, the first model is configured to perform a linear operation on the first information to obtain second information, perform a nonlinear operation on the second information to obtain third information, and obtain the first processing result based on the third information.

公开(公告)号：US20190317729A1

公开(公告)日：2019-10-17

申请号：US16455152

申请日：2019-06-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Bogdan CAUTIS ,  Ruiming TANG ,  Zhenhua DONG ,  Xiuqiang HE ,  Zhirong LIU

IPC: G06F7/24 ,  G06F17/18 ,  G06F3/0482

Abstract: An application sorting method and apparatus are provided. The method includes: obtaining, a positive operation probability and positive operation feedback information of each of at least two data samples; calculating an uncertainty parameter of a positive operation probability of a first data sample based on the positive operation probabilities and the positive operation feedback information of the at least two data samples and feature indication information of at least one same feature in a plurality of features in the at least two data samples; and correcting the positive operation probability of the first data sample by using the uncertainty parameter of the positive operation probability; and sorting, based on corrected positive operation probabilities, application programs corresponding to the at least two data samples.

公开(公告)号：US20230206076A1

公开(公告)日：2023-06-29

申请号：US18111066

申请日：2023-02-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yishi XU ,  Yingxue ZHANG ,  Huifeng GUO ,  Ruiming TANG ,  Yanhui GENG

IPC: G06N3/082 ,  G06N3/045

CPC classification number: G06N3/082 ,  G06N3/045

Abstract: System and method for training a recommender system (RS). The RS is configured to make recommendations in respect of a bipartite graph that comprises a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes, the RS including an existing graph neural network (GNN) model configured by an existing set of parameters. The method includes: applying a loss function to compute an updated set of parameters for an updated GNN model that is trained with a new graph using the first set of parameters as initialization parameters, the loss function being configured to distil knowledge based on node embeddings generated by the existing GNN model in respect of an existing graph, wherein the new graph includes a plurality of user nodes and a plurality of item nodes that are also included in the existing graph; and replacing the existing GNN model of the RS with the updated GNN model.

公开(公告)号：US20200242450A1

公开(公告)日：2020-07-30

申请号：US16850549

申请日：2020-04-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Ruiming TANG ,  Minzhe NIU ,  Yanru QU ,  Weinan ZHANG ,  Yong YU

IPC: G06N3/04 ,  G06N3/063 ,  G06K9/62 ,  G06F17/16

Abstract: Example user behavior prediction methods and apparatus are described. One example method includes obtaining a first contribution value of each piece of characteristic data for a specified behavior after obtaining behavior prediction information including a plurality of pieces of characteristic data. Every N pieces of characteristic data in the plurality of pieces of characteristic data may be processed by using one corresponding characteristic interaction model, to obtain a second contribution value of the every N pieces of characteristic data for the specified behavior. Finally, an execution probability of executing the specified behavior by a user may be determined based on the obtained first contribution value and the obtained second contribution value, to predict a user behavior. In the example method, interaction impact of the plurality of pieces of characteristic data on the specified behavior is considered during behavior prediction.

公开(公告)号：US20240242127A1

公开(公告)日：2024-07-18

申请号：US18620051

申请日：2024-03-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yichao WANG ,  Bo CHEN ,  Ruiming TANG ,  Xiuqiang HE ,  Hongkun ZHENG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: This application discloses an information recommendation method, which may be applied to the field of artificial intelligence. The method includes: obtaining a target feature vector; and processing the target feature vector by using a recommendation model, to obtain recommendation information, where the recommendation model includes a cross network, a deep network, and a target network; the target network is used to perform fusion processing on a first intermediate output that is output by the first cross layer and a second intermediate output that is output by the first deep layer, to obtain a first fusion result, and the target network is further used to: process the first fusion result to obtain a first weight corresponding to the first cross layer and a second weight corresponding to the first deep layer, and weight the first fusion result with the first weight and the second weight separately.

公开(公告)号：US20230306077A1

公开(公告)日：2023-09-28

申请号：US18327584

申请日：2023-06-01

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Huifeng GUO ,  Bo CHEN ,  Ruiming TANG ,  Zhenguo LI ,  Xiuqiang HE

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: Embodiments of this application provide a data processing method and apparatus to better learn a vector representation value of each feature value in a continuous feature. The method specifically includes: The data processing apparatus obtains the continuous feature from sample data, and then performs discretization processing on the continuous feature by using a discretization model, to obtain N discretization probabilities corresponding to the continuous feature. The N discretization probabilities correspond to N preset meta-embeddings, and N is an integer greater than 1. Finally, the data processing apparatus determines a vector representation value of the continuous feature based on the N discretization probabilities and the N meta-embeddings.

公开(公告)号：US20230153579A1

公开(公告)日：2023-05-18

申请号：US18154523

申请日：2023-01-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jianing SUN ,  Yingxue ZHANG ,  Guo Huifeng ,  Ruiming TANG ,  Xiuqiang HE ,  Dengcheng ZHANG ,  Han YUAN

IPC: G06N3/0464 ,  G06N3/08

CPC classification number: G06N3/0464 ,  G06N3/08

Abstract: Method and system for processing a bipartite graph that comprises a plurality of first nodes of a first node type, and a plurality of second nodes of a second type, comprising: generating a target first node embedding for a target first node based on features of second nodes and first nodes that are within a multi-hop first node neighbourhood of the target first node, the target first node being selected from the plurality of first nodes of the first node type; generating a target second node embedding for a target second node based on features of first nodes and second nodes that are within a multi-hop second node neighbourhood of the target second node, the target second node being selected from the plurality of second nodes of the second node type; and determining a relationship between the target first node and the target second node based on the target first node embedding and the target second node embedding.

公开(公告)号：US20220261591A1

公开(公告)日：2022-08-18

申请号：US17661448

申请日：2022-04-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Ruiming TANG ,  Huifeng GUO ,  Zhenguo LI ,  Xiuqiang HE

IPC: G06K9/62

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m≥3, and m>n≥2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.