US 11,836,782 B2
Personalized item recommendations through large-scale deep-embedding architecture with real-time inferencing
Aditya Mantha, Sunnyvale, CA (US); Yokila Arora, Sunnyvale, CA (US); Shubham Gupta, Sunnyvale, CA (US); Praveenkumar Kanumala, Newark, CA (US); Stephen Dean Guo, Saratoga, CA (US); and Kannan Achan, Saratoga, CA (US)
Assigned to WALMART APOLLO, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Sep. 3, 2021, as Appl. No. 17/466,277.
Application 17/466,277 is a continuation of application No. 16/777,571, filed on Jan. 30, 2020, granted, now 11,113,744.
Prior Publication US 2021/0398192 A1, Dec. 23, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0601 (2023.01); G06F 16/9035 (2019.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/9035 (2019.01); G06Q 30/0635 (2013.01); G06Q 30/0641 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform:
training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets, wherein the triplets each comprise a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket;
randomly sampling an anchor item from a category of items selected by a user; and
generating a list of complementary items using a query vector associated with the user and the anchor item, wherein the query vector is generated for the user and the anchor item using the two sets of item embeddings and the set of user embeddings.