公开(公告)号：US20230298080A1

公开(公告)日：2023-09-21

申请号：US18108916

申请日：2023-02-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tilman Drerup ,  Nour Alkhatib ,  Jonathan Gu ,  Amin Akbari ,  Changyao Chen

IPC: G06Q30/0601 ,  G06N3/092

CPC classification number: G06Q30/0617 ,  G06N3/092

Abstract: An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

公开(公告)号：US20250095055A1

公开(公告)日：2025-03-20

申请号：US18965960

申请日：2024-12-02

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jeffrey Bernard Arnold ,  Rob Donnelly ,  Sumit Garg ,  Jonathan Gu ,  Bill Lundberg ,  David Pal ,  Sharath Rao Karikurve ,  Peng Qi

IPC: G06Q30/0601 ,  G06F9/451 ,  G06Q30/02

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

公开(公告)号：US20230368236A1

公开(公告)日：2023-11-16

申请号：US17744526

申请日：2022-05-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tilman Drerup ,  Anne Moxie ,  Sophia Li ,  Vibin Kundukulam ,  Jonathan Gu ,  Ashley Denney

IPC: G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q30/0211 ,  G06Q30/0239 ,  G06Q30/0617

Abstract: An online concierge system uses a new treatment engine to score users for applying treatments of a new treatment type. The new treatment engine uses treatment models to generate treatment lift scores for the user. The new treatment engine applies an aggregation function model to the treatment lift scores to generate an aggregated lift score for the user. If the aggregated lift score exceeds a threshold, the new treatment engine applies a treatment of the new treatment type to the user. The new treatment engine trains the aggregation function model based on training examples used to train the treatment models. For a training example associated with a particular treatment type, the new treatment engine uses a target lift score generated by the treatment model for the treatment type to evaluate the performance of the aggregation function model, and to update the aggregation function model accordingly.