公开(公告)号：US20190087884A1

公开(公告)日：2019-03-21

申请号：US16198704

申请日：2018-11-21

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhirong LIU ,  Ruiming TANG ,  Zhenhua DONG ,  Xiuqiang HE ,  Guoxiang CAO

IPC: G06Q30/06 ,  G06F17/30

Abstract: The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.

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：US20210067244A1

公开(公告)日：2021-03-04

申请号：US17097519

申请日：2020-11-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhenhua DONG ,  Chao PAN ,  Zhiyong FENG ,  Xu ZHOU

IPC: H04B10/071 ,  H04B10/40 ,  H04B10/61 ,  H04B10/25

Abstract: This application discloses an optical signal transceiver apparatus, and belongs to the communications field. The apparatus includes: an optical signal generation module, configured to generate a to-be-sent optical signal and a local oscillator optical signal, where the to-be-sent optical signal includes an OTDR signal; an optical combining/splitting module, configured to: receive a to-be-processed optical signal from an optical fiber; and input the to-be-processed optical signal into an coherent receiving module; the coherent receiving module, configured to coherently receive the local oscillator optical signal and the to-be-processed optical signal to obtain a to-be-processed electrical signal; a signal processing module, configured to: obtain a first digital signal and a second digital signal from the to-be-processed electrical signal based on a signal frequency; process the first digital signal to obtain a communications code stream; and process the second digital signal to obtain information used to reflect a feature of the optical fiber.

公开(公告)号：US20230162005A1

公开(公告)日：2023-05-25

申请号：US18157277

申请日：2023-01-20

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Pengxiang CHENG ,  Zhenhua DONG ,  Xiuqiang HE ,  Xiaolian ZHANG ,  Shi YIN ,  Yuelin HU

IPC: G06N3/045

CPC classification number: G06N3/045

Abstract: This application provides a neural network distillation method and apparatus in the field of artificial intelligence. The method includes: obtaining a sample set, where the sample set includes a biased data set and an unbiased data set, the biased data set includes biased samples, and the unbiased data set includes unbiased samples; determining a first distillation manner based on data features of the sample set, where, in the first distillation manner, a teacher model is trained by using the unbiased data set and a student model is trained by using the biased data set; and training a first neural network based on the biased data set and the unbiased data set in the first distillation manner, to obtain an updated first neural network.

公开(公告)号：US20230153857A1

公开(公告)日：2023-05-18

申请号：US18156512

申请日：2023-01-19

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jingjie LI ,  Hong ZHU ,  Zhenhua DONG ,  Xiaolian ZHANG ,  Shi YIN ,  Xinhua FENG ,  Xiuqiang HE

IPC: G06Q30/0251 ,  G06Q30/0202

CPC classification number: G06Q30/0251 ,  G06Q30/0202

Abstract: A training method includes: obtaining a first recommendation model, where a model parameter of the first recommendation model is obtained through training based on n first training samples; determining an impact function value of each first training sample with respect to a verification loss of m second training samples in the first recommendation model; determining, based on the impact function value of each first training sample with respect to the verification loss, a weight corresponding to each first training sample; and training the first recommendation model based on the n first training samples and the weights corresponding to the n first training samples, to obtain a target recommendation model.

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：US20250077873A1

公开(公告)日：2025-03-06

申请号：US18949212

申请日：2024-11-15

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jieming ZHU ,  Liqun DENG ,  Zhou ZHAO ,  Dong YAO ,  Zhenhua DONG

IPC: G06N3/084 ,  G06F18/214

Abstract: Representation learning methods and related devices are provided. An example method includes: obtaining a dataset of to-be-learned data; inputting the dataset into an encoder, and extracting features of the data segments based on a parameter of the encoder to obtain representation vectors corresponding to data segments of various scales; inputting the representation vectors into an interaction module, and performing, based on a parameter of the interaction module, information interaction on representation vectors corresponding to data segments of adjacent scales in the subset, to obtain fused representation vectors corresponding to the data segments of various scales; constructing an objective function based on the fused representation vectors; and optimizing the objective function to adjust the parameter of the encoder and the parameter of the interaction module, so that the encoder and the interaction module learn a high-quality representation vector of the to-be-learned data.

公开(公告)号：US20230088171A1

公开(公告)日：2023-03-23

申请号：US17989719

申请日：2022-11-18

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Guohao CAI ,  Gang WANG ,  Zhenhua DONG ,  Xiaoguang LI ,  Xiuqiang HE ,  Hong ZHU

IPC: G06F16/9535 ,  G06F16/954

Abstract: A method and an apparatus for training a search recommendation model, and a method and an apparatus for sorting search results are provided. The training method includes: obtaining a training sample set including a sample user behavior group sequence and a masked sample user behavior group sequence; and using the training sample set as input data, and training a search recommendation model, to obtain a trained search recommendation model, where a target of the training is to obtain the object of the response operation of the sample user after the mask processing, the search recommendation model is used to predict a label of a candidate recommendation object in search results corresponding to a query field when a target user inputs the query field, and the label is used to indicate a probability that the target user performs a response operation on the candidate recommendation object.

公开(公告)号：US20210248651A1

公开(公告)日：2021-08-12

申请号：US17242588

申请日：2021-04-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Chih Yao CHANG ,  Hong ZHU ,  Zhenhua DONG ,  Xiuqiang HE ,  Bowen YUAN

IPC: G06Q30/02 ,  G06K9/62 ,  G06N20/00

Abstract: This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.

公开(公告)号：US20200258006A1

公开(公告)日：2020-08-13

申请号：US16863110

申请日：2020-04-30

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Fei CHEN ,  Zhenhua DONG ,  Zhenguo LI ,  Xiuqiang HE ,  Li QIAN ,  Shuaihua PENG

IPC: G06N20/00 ,  G06N5/02

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.