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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.

Patent No.:

US 10367862 B1

PDFFull Text
Date of Patent:
Jul. 30, 2019

Filed:

Dec. 01, 2016

Large-scale page recommendations on online social networks

Applicant:

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

Inventors:

Jun Li, Sammamish, WA (US);

Fei Guo, Redmond, WA (US);

Bradley Green, Snohomish, WA (US);

Assignee:

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

Attorney:

Baker Botts L.L.P.

Primary Examiner:

Nicholas R Taylor

Assistant Examiner:

Ho T Shiu

Int. Cl.
CPC ...
G06F 17/30 (2006.01); G06F 7/00 (2006.01); G06Q 30/00 (2012.01); H04L 29/06 (2006.01); G06F 16/248 (2019.01); G06F 16/2457 (2019.01); G06F 3/00 (2006.01); G06Q 50/00 (2012.01); H04N 21/00 (2011.01); G06Q 30/02 (2012.01); H04L 29/08 (2006.01); G06Q 10/10 (2012.01); G06Q 10/06 (2012.01); H04W 4/21 (2018.01); G06F 16/24 (2019.01); G06F 16/28 (2019.01); G06F 16/903 (2019.01); H04N 21/25 (2011.01); H04N 21/4788 (2011.01);
U.S. Cl.
CPC ...
H04L 65/403 (2013.01); G06F 3/00 (2013.01); G06F 16/248 (2019.01); G06F 16/24578 (2019.01); G06Q 30/0269 (2013.01); G06Q 50/01 (2013.01); H04N 21/00 (2013.01); G06F 16/24 (2019.01); G06F 16/282 (2019.01); G06F 16/903 (2019.01); G06Q 10/0633 (2013.01); G06Q 10/10 (2013.01); G06Q 30/02 (2013.01); H04L 67/02 (2013.01); H04L 67/306 (2013.01); H04N 21/252 (2013.01); H04N 21/4788 (2013.01); H04W 4/21 (2018.02);
Abstract

In one embodiment, a method includes accessing user-concept scores for a first set of users, wherein each user-concept score is associated with a user-concept pair; calculating recommended user-concept scores for a subset of user-concept pairs in a second set of users. The first set of users may be discrete from the second set of users. A recommendation-algorithm may compute the recommended user-concept scores for a user-concept pair by optimizing an objective function comprising a plurality of predicted rating functions. Each predicted rating function may be determined using a user score, a concept score, a user-bias value associated with the user, as well as a concept-bias value associated with the concept. Finally, the method may include sending recommendations for one or more concepts based on the recommended user-concept scores for the second set of users.

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