公开(公告)号：US20250147958A1

公开(公告)日：2025-05-08

申请号：US19012864

申请日：2025-01-08

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/242 ,  G06F16/2457 ,  G06F16/28 ,  G06F18/214 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US12222937B2

公开(公告)日：2025-02-11

申请号：US17668358

申请日：2022-02-09

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/242 ,  G06F16/2457 ,  G06F16/28 ,  G06F18/214 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20230306023A1

公开(公告)日：2023-09-28

申请号：US17668358

申请日：2022-02-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/2457 ,  G06F16/242 ,  G06F16/28 ,  G06N20/00 ,  G06K9/62

CPC classification number: G06F16/24534 ,  G06F16/2448 ,  G06F16/24578 ,  G06F16/283 ,  G06K9/6257 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20230252549A1

公开(公告)日：2023-08-10

申请号：US18107854

申请日：2023-02-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Yuqing Xie ,  Taesik Na ,  Saurav Manchanda

IPC: G06Q30/0601 ,  G06Q30/0201

CPC classification number: G06Q30/0631 ,  G06Q30/0201

Abstract: To train an embedding-based model to determine relevance between items and queries, an online system generates training data from previously received queries and interactions with results for the queries. The training data includes positive training examples including a query and an item with which a user performed a specific interaction after providing the query. To generate negative training examples for the query to include in the training data, the online system determines measures of similarity between items with which the specific interaction was not performed and the query. The online system may weight a loss function for the embedding-based model by the measure of similarity for a negative example, increasing the effect of a negative example including a query and an item with a larger measure of similarity. In other embodiments, the online system selects negative training examples based on the measures of similarities between items and queries in pairs.