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:
Feb. 18, 2020

Filed:

Apr. 06, 2017
Applicant:

Ping an Technology (Shenzhen) Co., Ltd., Shenzhen, CN;

Inventors:

Luyang Cao, Shenzhen, CN;

Jianming Wang, Shenzhen, CN;

Jing Xiao, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 5/02 (2006.01); G06Q 30/02 (2012.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 5/022 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0255 (2013.01); G06Q 30/0271 (2013.01);
Abstract

A method of collaborative filtering in combination with time factor includes: establishing an exponential smoothing model; acquiring a time period proposed for the exponential smoothing model, the time period includes a plurality of time cycles; acquiring a plurality of user identifiers and user preference degree values of the user identifiers over a specified product during the plurality of time cycles; performing iterative calculations of the user preference degree values utilizing the exponential smoothing model, and obtaining smoothing results corresponding to the time cycles; generating a sparse matrix utilizing the user identifiers and the smoothing result corresponding to the time cycles, the sparse matrix includes a plurality of user preference degrees to be predicted; acquiring a collaborative filtering model and inputting the smoothing results corresponding to the time cycles into the collaborative filtering model; and training through the collaborative filtering model, calculating and obtaining predictive values of the plurality of user preference degrees to be predicted in the sparse matrix.


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