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：US20250068988A1

公开(公告)日：2025-02-27

申请号：US18238330

申请日：2023-08-25

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Rustin Partow

IPC: G06Q10/04

Abstract: A computing system automatically selects treatments for users by generating a propensity vector for a set of treatments and selecting a treatment based on the propensity vector. The propensity vector is determined based on one or more computer models that predict user actions responsive to the treatments and the propensity vector is determined based on the value of a treatment parameter. The treatment parameter is perturbed to determine an adjusted propensity vector. Treatments are applied and outcomes determined with the propensities determined by the current value of the treatment parameter, and counterfactuals for the adjusted treatment vector are determined to evaluate the effect of modifying the treatment parameter. When the perturbed treatment parameter value yields improved results in the counterfactual, the current value is modified to improve performance of the model as a whole without requiring retraining of underlying predictive models.

公开(公告)号：US20250045673A1

公开(公告)日：2025-02-06

申请号：US18228669

申请日：2023-07-31

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Rustin Partow ,  Tilman Drerup

IPC: G06Q10/0637 ,  G06Q10/0639

Abstract: An embedding model is trained to learn latent representations of users describing information related to conditional treatment effect for users relative to different potential treatments. The user embeddings may be used to determine the types of situations in which a user responds differently to different conditions or situations. To train this model, a plurality of experiments with users may be performed to determine user responses to different treatment conditions in the experiments. The conditional treatment effect for users in the experiments may be determined, e.g., with counterfactual predictions of a treatment not experienced by a user in the experiment. The embedding model may be trained with decoders that each predict the conditional treatment effect with respect to one of the experiments, enabling a loss for each experiment with respect to the conditional treatment effect to jointly train the embedding model.

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.