公开(公告)号：US20250069126A1

公开(公告)日：2025-02-27

申请号：US18236342

申请日：2023-08-21

Applicant: Maplebear Inc.

Inventor： Madeline Mesard ,  Kevin McCullough ,  Ryan Martin ,  Justin Park

IPC: G06Q30/0601 ,  G06F16/22 ,  G06Q10/087

Abstract: An online system identifies recipes that are most likely to be pertinent to particular users of the system. To do so, the online system uses an association table containing degrees of association between pairs of possible ingredients, identifying degrees of association between the constituent ingredients of various possible recipes and between ingredients from known user personalization data about the user to whom recipes are being recommended. These degrees of association are used to compute a score for each recipe as a whole, with the highest scores indicating the most pertinent recipes for the user in question. The most pertinent recipes, and/or the constituent ingredients of those recipes, are recommended to the user, and the system may additionally aid the user in obtaining the full complement of ingredients for a recommended recipe. The system may also build the association table as a co-occurrence graph of pairs of items that were previously purchased together by users of the system.

公开(公告)号：US20240070393A1

公开(公告)日：2024-02-29

申请号：US17821889

申请日：2022-08-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shishir Kumar Prasad ,  Cameron Nicholas Taylor ,  John Salaveria ,  Joey Loi ,  Kevin McCullough

IPC: G06F40/284 ,  G06Q30/06

CPC classification number: G06F40/284 ,  G06Q30/0621

Abstract: An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.