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:
May. 27, 2025

Filed:

Jun. 29, 2021
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Yudan Liu, Shenzhen, CN;

Kaikai Ge, Shenzhen, CN;

Xu Zhang, Shenzhen, CN;

Leyu Lin, Shenzhen, CN;

Xin Chen, Shenzhen, CN;

Xiaobo Hao, Shenzhen, CN;

Wei Wang, Shenzhen, CN;

Kai Zhuang, Shenzhen, CN;

Su Yan, Shenzhen, CN;

Zhida Pan, Shenzhen, CN;

Linyao Tang, Shenzhen, CN;

Jing Zhang, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2023.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G06N 3/084 (2013.01);
Abstract

This application discloses a content recommendation method performed at a computer device and belongs to the field of artificial intelligence. The method includes: acquiring a target user vector of a target user; determining n groups of seed user vectors according to the target user vector, each group of seed user vectors corresponding to a respective piece of candidate recommendation content; invoking a look-alike model to calculate a similarity between the target user vector and each group of seed user vectors, the look-alike model being used for calculating a similarity between user vectors based on an attention mechanism; and determining, among the n pieces of candidate recommendation content, target content to be recommended to the target user according to the respective similarities of the corresponding n groups of seed user vectors. This application can resolve a problem of relatively low accuracy of a recommendation method in the related art.


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