US 12,266,006 B2
Providing and displaying search results in response to a query
Taesik Na, Seattle, WA (US); Vinesh Reddy Gudla, South San Francisco, CA (US); and Xiao Xiao, San Diego, CA (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Jan. 25, 2023, as Appl. No. 18/159,357.
Prior Publication US 2024/0249335 A1, Jul. 25, 2024
Int. Cl. G06Q 30/06 (2023.01); G06F 16/9535 (2019.01); G06Q 30/0201 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/9535 (2019.01); G06Q 30/0201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising, at a computer system comprising a processor and a computer-readable medium:
receiving a query from a user device corresponding to a user of an online concierge system, wherein the query includes free text;
generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine-learning model that is trained to generate query embeddings based on free text from queries;
accessing a set of candidate items;
computing a relevance score for each candidate item of the set of candidate items, wherein computing a relevance score for a candidate item comprises:
accessing an item embedding for the candidate item stored by the online concierge system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and
computing the relevance score for the candidate item based on the query embedding and the item embedding;
computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises:
accessing a user embedding associated with the user, where the user embedding is stored by the online concierge system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data;
accessing an item embedding for the candidate item stored by the online concierge system, wherein the item embedding is generated by a second item embedding model that is trained to generate item embeddings based on item data; and
computing the personalization score for the candidate item based on the user embedding and the item embedding;
computing a query specificity score for the query, wherein the query specificity score is computed based on an entropy score for the query, wherein the entropy score describes historical user interactions with items in search results and represents, for a set of possible interaction outcomes, an uncertainty in which interaction outcome may result from the query, wherein an interaction outcome represents a user interaction with an item presented in search results for the query;
adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score;
computing a ranking score for each candidate item of the set of candidate items, wherein the ranking score for a candidate item is computed based on the relevance score for each candidate item and the adjusted personalization score for each candidate item;
ranking the candidate items based on the ranking scores; and
transmitting the set of candidate items for display on the user device based on the ranking.