US 11,966,405 B1
Inferring brand similarities using graph neural networks and selection prediction
Chaoran Wei, New York, NY (US); Shaunak Mishra, Jersey City, NJ (US); Anirban Sengupta, Sammamish, WA (US); and Ravendar Lal, Bellevue, WA (US)
Assigned to AMAZON TECHNOLOGIES, INC., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Dec. 12, 2022, as Appl. No. 18/064,591.
Int. Cl. G06F 16/2457 (2019.01); G06N 3/045 (2023.01)
CPC G06F 16/24578 (2019.01) [G06N 3/045 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, wherein when executed the program causes the at least one computing device to at least:
generate a brand-to-brand graph indicating similarities between a set of brands according to click-through data, wherein edges of the brand-to-brand graph represent a respective probability of a click-through to an item detail page associated with an item of a given brand associated with a first node following search queries associated with another brand associated with a second node;
receive a search query;
identify a first brand based at least in part on the search query;
analyze, using a first graph convolutional network (GCN) tower, the brand-to-brand graph to determine brand similarities among the first brand and a first set of other brands;
analyze, using a second GCN tower, the brand-to-brand graph to determine brand similarities among a second brand and a second set of other brands;
determine a level of similarity between the first brand and the second brand based at least in part on an output of the first GCN tower and an output of the second GCN tower; and
generate a user interface in response to the search query, wherein the user interface includes one or more items from the second brand that do not match the search query based at least in part on the level of similarity.