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    CUSTOMIZATION OF REPLACEMENT ITEMS USING MODEL WITH CONTEXTUAL FEATURES
    22.
    发明申请
    CUSTOMIZATION OF REPLACEMENT ITEMS USING MODEL WITH CONTEXTUAL FEATURES 有权

    公开(公告)号：US20250061505A1

    公开(公告)日：2025-02-20

    申请号：US18233826

    申请日：2023-08-14

    Applicant: Maplebear Inc.

    Inventor： Sharath Rao Karikurve ,  Ramasubramanian Balasubramanian

    IPC: G06Q30/0601 ,  G06F40/40 ,  H04L51/046

    Abstract: An online concierge system scores candidate replacement items for an ordered item that is not available for delivery. A set of contextual features may be generated describing the user and/or the order in which the item is being replaced, enabling the recommended items to be evaluated with additional context and more-correctly evaluate whether a customer will accept a replacement item, particularly when the replacement item is selected by a picker or the online concierge system. In addition, as candidate replacement items may receive feedback from the customer in different contexts, during training the candidate items may be labeled with different values according to a hierarchy based on the particular feedback and context provided by the user.

    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING
    24.
    发明公开
    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20240330695A1

    公开(公告)日：2024-10-03

    申请号：US18129023

    申请日：2023-03-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

    IPC: G06N3/092 ,  G06N3/04

    CPC classification number: G06N3/092 ,  G06N3/04

    Abstract: A reinforcement learning model selects a content composition based, in part, on inter-session rewards. In addition to near-in-time rewards of user interactions with a content composition for evaluating possible actions, the reinforcement learning model also generates a reward and/or penalty based on between-session information, such as the time between sessions. This permits the reinforcement learning model to learn to evaluate content compositions not only on the immediate user response, but also on the effect of future user engagement. To determine a composition for a search query, the reinforcement learning model generates a state representation of the user and search query and evaluates candidate content compositions based on learned parameters of the reinforcement learning model that evaluates inter-session rewards of the content compositions.

    TRAINED MACHINE LEARNING MODELS FOR PREDICTING REPLACEMENT ITEMS USING EXPIRATION DATES
    25.
    发明公开
    TRAINED MACHINE LEARNING MODELS FOR PREDICTING REPLACEMENT ITEMS USING EXPIRATION DATES 审中-公开

    公开(公告)号：US20240289855A1

    公开(公告)日：2024-08-29

    申请号：US18113965

    申请日：2023-02-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Ramasubramanian Balasubramanian ,  Sharath Rao Karikurve

    IPC: G06Q30/0601 ,  G06N3/08

    CPC classification number: G06Q30/0613 ,  G06N3/08

    Abstract: A specific item is identified to suggest a replacement therefor to a user. A set of candidate replacement items for the specific item is determined. For at least one of the candidate replacement items, an expiration score is determined based on expiration information associated with the item. A replacement score for the candidate replacement item is determined by inputting the determined expiration score as a feature into a machine learning model that is trained using features of historical samples of candidate replacement items suggested as a replacement to users and the replacement suggestion being accepted by the users. One or more of the candidate replacement items is selected based on respective replacement scores as one or more suggested replacement items. A graphical user interface of a client device of the user is caused to display the one or more suggested replacement items as the replacement for the specific item.

    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
    27.
    发明申请
    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL 有权

    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.

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