公开(公告)号：US20240265309A1

公开(公告)日：2024-08-08

申请号：US18638414

申请日：2024-04-17

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Biao LU ,  Jieming ZHU ,  Xiuqiang HE ,  Zhaowei WANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: This application relates to the artificial intelligence field, and in particular, to an item recommendation method and apparatus, and a storage medium. The method includes: obtaining historical interaction data of a target object, where the historical interaction data indicates a historical interaction event between the target object and at least one item; obtaining a pre-trained target recommendation model, where the target recommendation model includes a graph neural network model with one convolutional layer, and the convolutional layer indicates an association relationship between a sample object and a sample item; and invoking, based on the historical interaction data, the target recommendation model to output a target item corresponding to the target object. In the embodiments of this application, a framework structure of the target recommendation model is simplified, so that model operation time is greatly reduced.

公开(公告)号：US20240184837A1

公开(公告)日：2024-06-06

申请号：US18441389

申请日：2024-02-14

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jieming ZHU ,  Zhou ZHAO ,  Shengyu ZHANG ,  Xiuqiang HE ,  Li QIAN

IPC: G06F16/9535 ,  G06V10/40

CPC classification number: G06F16/9535 ,  G06V10/40

Abstract: Examples of recommendation methods and apparatus are described. In one example method, a plurality of images are obtained, where each image includes one candidate interface and one type of candidate content presented by using the candidate interface. Image feature data of each image is obtained, and input for a prediction model is determined based on user feature data of a target user and the image feature data. Then, a degree of preference of the target user for each image is predicted by using the prediction model. At least one of a candidate interface or candidate content that are included in the plurality of images is selected based on the degree of preference. Recommendation is then performed to the user based on the selected candidate content or candidate interface.

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.