US 12,112,365 B2
Probabilistic determination of compatible content
Brian Johnson, San Francisco, CA (US); John Milinovich, San Francisco, CA (US); Lance Alan Riedel, Menlo Park, CA (US); Andrew Look, San Francisco, CA (US); Mukund Narasimhan, Bellevue, WA (US); and Yu Liu, Los Altos, CA (US)
Assigned to Pinterest, Inc., San Francisco, CA (US)
Filed by Pinterest, Inc., San Francisco, CA (US)
Filed on May 27, 2022, as Appl. No. 17/827,359.
Application 17/827,359 is a continuation of application No. 15/957,822, filed on Apr. 19, 2018, granted, now 11,373,230.
Prior Publication US 2022/0284501 A1, Sep. 8, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/06 (2023.01); G06F 16/957 (2019.01); G06Q 30/0201 (2023.01); G06Q 30/0282 (2023.01); G06Q 30/0601 (2023.01); G06Q 50/00 (2012.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/9577 (2019.01); G06Q 30/0201 (2013.01); G06Q 30/0282 (2013.01); G06Q 50/01 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
determining, by a computing system and in response to a user created digital content collection created by a user and maintained by the computing system, a feature vector associated with the user created digital content collection, wherein the user created digital content collection includes a first plurality of digital content items having different digital content types and the feature vector is determined based at least in part on a computer-implemented analysis of the first plurality of digital content items;
accessing, by the computing system, a plurality of representative digital content collections organized as a taste graph to identify a first plurality of representative digital content collections from the plurality of representative digital content collections based at least in part on the feature vector and a plurality of representative feature vectors associated with the plurality of representative digital content collections, wherein:
the plurality of representative feature vectors are determined based at least in part on corresponding computer-implemented analyses of the plurality of representative digital content collections; and
the taste graph is unique to the user;
determining, by the computing system, a first representative digital content collection from the plurality of representative digital content collections based at least in part on a similarity between the feature vector and a first representative feature vector associated with the first representative digital content collection, wherein the first representative digital content collection includes a second plurality of digital content items having different digital content types; and
identifying, by the computing system and based at least in part on the first representative digital content collection, a first digital content item from a corpus of digital content items as a recommended digital content item, wherein the first digital content item is not included in the first plurality of digital content items.