公开(公告)号：US20240220859A1

公开(公告)日：2024-07-04

申请号：US18393349

申请日：2023-12-21

Applicant: Maplebear Inc.

Inventor： Jonathan Gu ,  Bo Xiao ,  Yixi Ouyang ,  Jennifer Wiersema ,  Sophia Li ,  Matias Cersosimo ,  Rustin Partow ,  Levi Boxell ,  Tilman Drerup ,  Oleksii Stepanian

IPC: G06N20/00 ,  G06Q30/01

CPC classification number: G06N20/00 ,  G06Q30/01

Abstract: An online system uses an offline iterative clustering process to evaluate the performance of a set of content selection frameworks. To perform an iteration of the iterative clustering process, an online system clusters the testing example data into a set of clusters. An online system computes a set of framework scores for each of the generated clusters. An online system computes an improvement score for each cluster based on the performance scores of the clusters. To determine whether to perform another iteration, an online system computes an aggregated improvement score based on the improvement scores of the clusters. If an online system determines that the aggregated improvement score does not meet the threshold, an online system performs another iteration of the process above. When an online system finishes the iterative process, an online system outputs the improvement scores of the most-recent iteration.

公开(公告)号：US20240220805A1

公开(公告)日：2024-07-04

申请号：US18393338

申请日：2023-12-21

Applicant: Maplebear Inc.

Inventor： Jonathan Gu ,  Bo Xiao ,  Yixi Ouyang ,  Jennifer Wiersema ,  Sophia Li ,  Matias Cersosimo ,  Rustin Partow ,  Levi Boxell ,  Tilman Drerup ,  Oleksii Stepanian

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: A system accesses user data describing characteristics of a user and generates a content item score for each content item of a plurality of content items. The system generates the content item score by applying a machine-learning model to the user data, and then generates a plurality of content bundles. The system also generates a bundle score for each content bundle based on corresponding content item scores for the content item associated with each content bundle, randomly selects a bundle of the plurality of content bundles based on the generated bundle scores, and transmits the randomly selected bundle to a client device associated with the user for display to the user. Finally, the system applies the model to each of the generated training examples and updates the parameters of the model based on the model output.