公开(公告)号：US20240005132A1

公开(公告)日：2024-01-04

申请号：US17855366

申请日：2022-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Zhenbang Chen ,  Jingying Zhou ,  Peng Qi

IPC: G06N3/04 ,  G06N3/08 ,  G06Q30/06

CPC classification number: G06N3/0481 ,  G06N3/08 ,  G06Q30/0631

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.

公开(公告)号：US20230136886A1

公开(公告)日：2023-05-04

申请号：US17514177

申请日：2021-10-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Ramasubramanian Balasubramanian ,  Peng Qi

IPC: G06Q30/02 ,  G06Q10/08

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.