公开(公告)号：US20250139176A1

公开(公告)日：2025-05-01

申请号：US18496724

申请日：2023-10-27

Applicant: Maplebear Inc.

Inventor： Vinesh Reddy Gudla ,  David Vengerov ,  Tejaswi Tenneti

IPC: G06F16/9532 ,  G06Q30/0201 ,  G06Q30/0282 ,  G06Q30/0601

Abstract: A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.

公开(公告)号：US20250005644A1

公开(公告)日：2025-01-02

申请号：US18217324

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Yunzhi Ye ,  Han Li ,  David Vengerov ,  Allan Stewart ,  Aref Kashani Nejad

IPC: G06Q30/0601

Abstract: An online system accesses a two-tower model trained to identify candidate items for presentation to users, in which the model includes an item tower trained to compute item embeddings and a user tower trained to compute user embeddings. The user tower includes a long-term sub-tower trained to compute long-term embeddings for users and a short-term sub-tower trained to compute short-term embeddings for users. The model is trained based on item data associated with items, user data associated with users, and session data associated with user sessions. The system uses the item tower to compute an item embedding for each of multiple candidate items. The system also uses the long-term sub-tower to compute a long-term embedding for a user. The system then receives session data associated with a current session of the user and uses the short-term sub-tower to compute a short-term embedding for the user based on this session data.

公开(公告)号：US20250139681A1

公开(公告)日：2025-05-01

申请号：US18496720

申请日：2023-10-27

Applicant: Maplebear Inc.

Inventor： Vinesh Reddy Gudla ,  David Vengerov ,  Tejaswi Tenneti

IPC: G06Q30/0601

Abstract: A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system and described by query feature(s). The system obtains contextual feature(s) describing the query's context. The system applies a query processing model to the user query to determine a relevance score for each query result. The system applies a contextual bandit model to the query features and the contextual features to determine a weight vector for ranking parameters. The ranking parameters include relevance of a query result to the user query and dependability of the query result. The system determines, for each query result, a ranking score based on the weight vector and ranking parameter values of the query result. The system transmits the query results ranked according to the ranking scores for display on the client device.