US 12,259,894 B2
Accounting for item attributes when selecting items satisfying a query based on item embeddings and an embedding for the query
Taesik Na, Issaquah, WA (US); Zhihong Xu, Sunnyvale, CA (US); Guanghua Shu, Sunnyvale, CA (US); Tejaswi Tenneti, Fremont, CA (US); and Haixun Wang, Palo Alto, CA (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Feb. 7, 2022, as Appl. No. 17/666,531.
Prior Publication US 2023/0252032 A1, Aug. 10, 2023
Int. Cl. G06F 16/2457 (2019.01); G06F 16/242 (2019.01)
CPC G06F 16/24578 (2019.01) [G06F 16/2438 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for improving a search interface using embeddings extracted from a neural network, the method comprising:
receiving, at an online system, a query directed at the search interface;
applying the neural network, by the online system, to extract an embedding for the query, the embedding for the query representing the query in a latent space of the neural network, wherein applying the neural network to extract the embedding for the query comprises:
storing the query as a query feature vector,
inputting the query feature vector into the neural network, and
extracting a query latent vector in a first hidden layer of the neural network;
applying the neural network to extract item embeddings for each of a plurality of items maintained in an item database by the online system, each item embedding corresponding to an item offered by the online system and representing the item in the latent space of the neural network, wherein applying the neural network to extract an item embedding for an item comprises:
storing the item as an item feature vector,
inputting the item feature vector into the neural network, and
extracting an item latent vector in a second hidden layer of the neural network;
comparing, in the latent space of the neural network, the embedding for the query to the item embeddings to select a set of items corresponding to item embeddings that are selected in the latent space;
determining a value of a category associated with each item of the set of items from the item database;
generating a whitelist of values for the category based on the values of the category associated with each item of the set; and
generating, as a response to the query directed at the search interface that relies on the embeddings from the neural network, a query result comprising a plurality of items, wherein generating the plurality of items comprises removing one or more items having values for the category that are not included in the whitelist of values for the category.