公开(公告)号：US20230186361A1

公开(公告)日：2023-06-15

申请号：US17550960

申请日：2021-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user’s search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

公开(公告)号：US20230135683A1

公开(公告)日：2023-05-04

申请号：US17513739

申请日：2021-10-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06Q30/06 ,  G06Q30/02

Abstract: An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.