The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 16, 2024

Filed:

Apr. 28, 2021
Applicant:

Huawei Technologies Co., Ltd., Guangdong, CN;

Inventors:

Chih Yao Chang, Shenzhen, CN;

Hong Zhu, Shenzhen, CN;

Zhenhua Dong, Shenzhen, CN;

Xiuqiang He, Shenzhen, CN;

Bowen Yuan, Taipei, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/10 (2023.01); G06F 16/9535 (2019.01); G06F 18/10 (2023.01); G06F 18/20 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06Q 10/06 (2023.01); G06Q 30/02 (2023.01); G06Q 30/0282 (2023.01); G06Q 30/06 (2023.01);
U.S. Cl.
CPC ...
G06F 16/9535 (2019.01); G06F 18/10 (2023.01); G06F 18/214 (2023.01); G06F 18/285 (2023.01); G06N 20/00 (2019.01); G06Q 30/0282 (2013.01);
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.


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