GENERATING TRAINING DATA FOR A NUTRITIONAL REPLACEMENT MACHINE-LEARNING MODEL

    公开(公告)号:US20250069723A1

    公开(公告)日:2025-02-27

    申请号:US18455498

    申请日:2023-08-24

    Applicant: Maplebear Inc.

    Abstract: The online concierge system accesses item data for a target item and item data for a candidate item. The online concierge system generates a replacement score based on the accessed item data and generates a nutrition score based on the item data for the candidate item. The online concierge system generates a nutrition replacement score based on the replacement score and the nutrition score and stores a training example based on the item data and the nutrition replacement score. The training example may include the item data for the target item and the candidate item and a label based on the nutrition replacement score.

    IDENTIFYING ITEM SIMILARITY AND LIKELIHOOD OF SELECTION FOR LARGER-SIZE VARIANTS OF ITEMS ORDERED BY CUSTOMERS OF AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20240420210A1

    公开(公告)日:2024-12-19

    申请号:US18211107

    申请日:2023-06-16

    Abstract: An online concierge system receives information describing items in orders placed by a customer and a sequence of events associated with each order and identifies an impulse item included in the orders based on a set of rules, attributes of each item, and/or the sequence of events. The system applies a model to predict a measure of similarity between the impulse item and each of multiple candidate items and identifies larger-size variants of the impulse item based on this prediction and attributes of the impulse item and each candidate item. The system applies another model to predict a likelihood the customer will order each variant, computes a recommendation score for each variant based on this prediction, and determines whether to recommend each variant based on the score. Based on the determination, the system generates and sends a recommendation for a variant to a client device associated with the customer.

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