知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

5 records (took 0.014 seconds)

    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
    3.
    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.

Patent Agency Ranking