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:
Mar. 11, 2025

Filed:

May. 24, 2021
Applicant:

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

Inventors:

Zhijie Qiu, Shenzhen, CN;

Jun Rao, Shenzhen, CN;

Yi Liu, Shenzhen, CN;

Zhou Su, Shenzhen, CN;

Shukai Liu, Shenzhen, CN;

Zhenlong Sun, Shenzhen, CN;

Qi Liu, Shenzhen, CN;

Liangdong Wang, Shenzhen, CN;

Tiantian Shang, Shenzhen, CN;

Mingfei Liang, Shenzhen, CN;

Lei Chen, Shenzhen, CN;

Bo Zhang, Shenzhen, CN;

Leyu Lin, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/10 (2006.01); G06F 17/16 (2006.01); G06F 18/213 (2023.01); G06N 3/04 (2023.01); G06N 3/042 (2023.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); H04L 67/306 (2022.01); H04L 67/50 (2022.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
H04L 67/306 (2013.01); G06F 18/213 (2023.01); G06N 3/042 (2023.01); G06V 10/806 (2022.01); G06V 10/82 (2022.01); H04L 67/535 (2022.05); G06V 40/20 (2022.01);
Abstract

Embodiments of this application provide a method for making recommendations to a user and an apparatus, a computing device, and a storage medium. The method includes obtaining user attribute information, reading attribute information, reading history information, and candidate items; performing intra-group information fusion on the reading attribute information according to preset groupings to obtain reading feature information; obtaining a reading history weight according to the reading history information; obtaining history feature information according to the reading history weight and the reading history information; obtaining user feature information according to the user attribute information, the reading feature information, and the history feature information; and selecting a recommendation item from the candidate items according to the user feature information.


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