公开(公告)号：US20240172205A1

公开(公告)日：2024-05-23

申请号：US18431229

申请日：2024-02-02

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hong ZHU ,  Zhou XU ,  Liwen ZHANG

IPC: H04W72/0453 ,  H04W72/0457 ,  H04W72/51 ,  H04W76/20

CPC classification number: H04W72/0453 ,  H04W72/0457 ,  H04W72/51 ,  H04W76/20

Abstract: This application provides a carrier configuration method and a communication apparatus. The method may include: a terminal device accesses a first cell by using a control channel resource of a first carrier. The terminal device receives first carrier configuration information in the first cell. The first carrier configuration information is used to change a carrier of the terminal device from the first carrier to a second carrier in the first cell. The first carrier and the second carrier share the control channel resource, and include different spectrum resources. According to this application, spectrum resource utilization of the first cell can be improved.

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：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.