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:
Dec. 29, 2020

Filed:

May. 03, 2017
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Yi Miao, Redmond, WA (US);

Kelly Jayne Mayes, San Francisco, CA (US);

Eric Carl Ertmann, San Francisco, CA (US);

Li Ju, Issaquah, WA (US);

Congle Zhang, Seattle, WA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/06 (2012.01); G06F 16/2457 (2019.01); G06F 16/28 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 50/00 (2012.01); G06N 5/04 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 16/24578 (2019.01); G06F 16/287 (2019.01); G06F 16/9535 (2019.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06Q 30/0631 (2013.01); G06Q 50/01 (2013.01); H04L 67/18 (2013.01);
Abstract

An online system provides group recommendations by applying a set of sourcing rules to identify a plurality of candidate groups and then generating scores for the candidate groups. The sourcing rules can be configured to identify a relatively small subset of the groups maintained by the online system. After the candidate groups are identified, the online system generates a score for each candidate group, ranks the candidate groups based on the scores, and sends high-ranking candidate groups to the target user to be displayed as recommended groups. As a result, the online system generates a smaller number of scores, which advantageously allows for the online system to provide group recommendations to users in a more computationally efficient manner.


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