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    RECOMMENDATION SYSTEM USING USER EMBEDDINGS HAVING STABLE LONG-TERM COMPONENT AND DYNAMIC SHORT-TERM SESSION COMPONENT
    1.
    发明申请
    RECOMMENDATION SYSTEM USING USER EMBEDDINGS HAVING STABLE LONG-TERM COMPONENT AND DYNAMIC SHORT-TERM SESSION COMPONENT 有权

    公开(公告)号：US20250005644A1

    公开(公告)日：2025-01-02

    申请号：US18217324

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Chuanwei Ruan ,  Yunzhi Ye ,  Han Li ,  David Vengerov ,  Allan Stewart ,  Aref Kashani Nejad

    IPC: G06Q30/0601

    Abstract: An online system accesses a two-tower model trained to identify candidate items for presentation to users, in which the model includes an item tower trained to compute item embeddings and a user tower trained to compute user embeddings. The user tower includes a long-term sub-tower trained to compute long-term embeddings for users and a short-term sub-tower trained to compute short-term embeddings for users. The model is trained based on item data associated with items, user data associated with users, and session data associated with user sessions. The system uses the item tower to compute an item embedding for each of multiple candidate items. The system also uses the long-term sub-tower to compute a long-term embedding for a user. The system then receives session data associated with a current session of the user and uses the short-term sub-tower to compute a short-term embedding for the user based on this session data.

    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
    2.
    发明公开
    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240362657A1

    公开(公告)日：2024-10-31

    申请号：US18767909

    申请日：2024-07-09

    Applicant: Maplebear Inc.

    Inventor： Chuanwei Ruan ,  Ramasubramanian Balasubramanian ,  Peng Qi

    IPC: G06Q30/0202 ,  G06Q10/087

    CPC classification number: G06Q30/0202 ,  G06Q10/087

    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.

    USER EMBEDDING GENERATION USING LLM-GENERATED CONTENT EMBEDDINGS
    4.
    发明申请
    USER EMBEDDING GENERATION USING LLM-GENERATED CONTENT EMBEDDINGS 有权

    公开(公告)号：US20250022036A1

    公开(公告)日：2025-01-16

    申请号：US18772774

    申请日：2024-07-15

    Applicant: Maplebear Inc.

    Inventor： Chuanwei Ruan ,  Allan Stewart ,  Li Tan ,  Yunzhi Ye ,  Aref Kashani Nejad

    IPC: G06Q30/0601 ,  G06N3/0475 ,  G06N3/09

    Abstract: An online system selects an item to present to a user of the online system. The online system accesses user interaction data for the user. The online system transmits the user interaction data to a model serving system and receives, from the model serving system, item embeddings for the items with which the user interacted. The model serving system may use an LLM to generate the item embeddings based on the user interaction data. The online system generates a user embedding array based on the item embeddings. The online system applies a transformer network to the user embedding array to generate a user embedding describing the user. To select an item to present to the user, the online system compares the generated user embedding to item embeddings for a set of candidate items. The online system selects a candidate item based on the interaction scores.

    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
    5.
    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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