公开(公告)号：US20240202771A1

公开(公告)日：2024-06-20

申请号：US18084938

申请日：2022-12-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ruhan Zhang ,  Jeffrey Moulton ,  Nicholas George Gordenier

IPC: G06Q30/0241 ,  G06Q30/0242

CPC classification number: G06Q30/0249 ,  G06Q30/0242 ,  G06Q30/0277

Abstract: An online concierge system may conduct experiments in presentation of prioritized items for content campaigns with offline simulations. The offline simulation may use a joint budget for the content campaign used by several experimental variations that affect prioritized content presentation. To correct for distortions that may occur from differing rates of budget use in the variations when the budget is reached before a total period for the experiment, the budget use of each variation is compared to a “fair value” to determine an adjustment to the metrics determined in the experiment. Variants that exceed the fair value may have their metrics capping to the portion allocable to a budget use that does not exceed the fair value, while variants that use less than the fair value may have the metrics extrapolated to account for the additional budget that would be available with a fair value budget.

公开(公告)号：US20250147997A1

公开(公告)日：2025-05-08

申请号：US18932301

申请日：2024-10-30

Applicant: Maplebear Inc.

Inventor： Xiaochen Wang ,  Taesik Na ,  Xiao Xiao ,  Ruhan Zhang ,  Xuan Zhang ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/35 ,  G06F16/383

Abstract: An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.