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    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System
    1.
    发明申请
    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System 有权

    公开(公告)号：US20250005381A1

    公开(公告)日：2025-01-02

    申请号：US18217356

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peng Qi ,  Cheng Jia ,  Xiyu Wang ,  Qiao Jiang ,  Sharad Gupta ,  David Pal ,  Joseph Haraldson ,  Zhenbang Chen

    IPC: G06N5/022

    Abstract: An online system manages presentation of content items in various presentation contexts such as when the users are browsing pages or when the users have entered a search query. The online system trains a single unified machine learning model that predicts one or more likelihoods of a target event associated with presentation of a content item in the different presentation contexts. The learned model is applied to a set of candidate content items associated with a presentation opportunity in a specific context. Features that are inapplicable to the specific context may be masked when applying the model. The online system may select between the candidate content items based on the predicted likelihoods using the model trained across the multiple different contexts, such that the prediction for one context may be based in part on learned outcomes in other related contexts.

    USING TRANSFER LEARNING TO REDUCE DISCREPANCY BETWEEN TRAINING AND INFERENCE FOR A MACHINE LEARNING MODEL
    2.
    发明公开
    USING TRANSFER LEARNING TO REDUCE DISCREPANCY BETWEEN TRAINING AND INFERENCE FOR A MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20230162038A1

    公开(公告)日：2023-05-25

    申请号：US17534184

    申请日：2021-11-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peng Qi ,  Zhenbang Chen

    IPC: G06N3/08 ,  G06N3/04 ,  G06Q30/02

    CPC classification number: G06N3/084 ,  G06N3/04 ,  G06Q30/0202

    Abstract: An online system uses a trained model predicting likelihoods of a user performing a specific interaction with items to order or to rank items for display to the user. The online system trains the model using interactions by users with items displayed by the online system. However, selection, popularity, and position from display of the items affects the model during training. To improve the model, the online system further trains the model using additional training data obtained from displaying items to users in different orders. The further training is done on a limited portion of the model, such as a limited number of layers of the model, to improve the model performance while reducing an amount of additional data to acquire to further train the model.

    TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM
    5.
    发明申请
    TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM 有权

    公开(公告)号：US20220398605A1

    公开(公告)日：2022-12-15

    申请号：US17343026

    申请日：2021-06-09

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Changyao Chen ,  Peng Qi ,  Weian Sheng

    IPC: G06Q30/02 ,  G06N3/04 ,  G06N3/08

    Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

    SELECTING AN ITEM FOR INCLUSION IN AN ORDER FROM A USER OF AN ONLINE CONCIERGE SYSTEM FROM A GENERIC ITEM DESCRIPTION RECEIVED FROM THE USER
    7.
    发明申请
    SELECTING AN ITEM FOR INCLUSION IN AN ORDER FROM A USER OF AN ONLINE CONCIERGE SYSTEM FROM A GENERIC ITEM DESCRIPTION RECEIVED FROM THE USER 有权

    公开(公告)号：US20220358560A1

    公开(公告)日：2022-11-10

    申请号：US17308993

    申请日：2021-05-05

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Weian Sheng ,  Peng Qi ,  Changyao Chen

    IPC: G06Q30/06 ,  G06N20/00 ,  G06N5/04

    Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.

    USER INTERFACE THAT PRE-POPULATES ITEMS IN AN ORDER MODULE FOR A USER OF AN ONLINE CONCIERGE SYSTEM USING A PREDICTION MODEL
    8.
    发明申请
    USER INTERFACE THAT PRE-POPULATES ITEMS IN AN ORDER MODULE FOR A USER OF AN ONLINE CONCIERGE SYSTEM USING A PREDICTION MODEL 有权

    公开(公告)号：US20220335493A1

    公开(公告)日：2022-10-20

    申请号：US17232651

    申请日：2021-04-16

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Weian Sheng ,  Peng Qi ,  Changyao Chen

    IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q10/08 ,  G06N7/00

    Abstract: An online concierge system maintains historical orders received from a user that include one or more items. For items included in one more historical orders, the online concierge system determines an interval between orders including an item, providing an indication of a frequency with which the user orders the item. When the online concierge system receives a request to create an order from the user, in response to an amount of time between a most recently received order including the item and a time when the request was received is within a threshold duration of the interval between orders including the item, the online concierge system selects an item from a category including the item. The selected item may be the item or an alternative item in the category. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item.

    MACHINE-LEARNED NEURAL NETWORK ARCHITECTURES FOR INCREMENTAL LIFT PREDICTIONS USING EMBEDDINGS
    10.
    发明公开
    MACHINE-LEARNED NEURAL NETWORK ARCHITECTURES FOR INCREMENTAL LIFT PREDICTIONS USING EMBEDDINGS 审中-公开

    公开(公告)号：US20240005377A1

    公开(公告)日：2024-01-04

    申请号：US17855377

    申请日：2022-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Zhenbang Chen ,  Jingying Zhou ,  Peng Qi

    IPC: G06Q30/06 ,  G06Q30/02

    CPC classification number: G06Q30/0631 ,  G06Q30/0222 ,  G06Q30/0205

    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.

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