US 12,266,005 B2
Identifying candidate replacement items with a source similarity score
Ramasubramanian Balasubramanian, San Francisco, CA (US); Lynn Fink, Chicago, IL (US); Alexandra Hart, Hinsdale, IL (US); Sanam Alavizadeh, Chicago, IL (US); Lauren Scully, San Francisco, CA (US); Samuel Lederer, Brooklyn, NY (US); Anna Vitti, Brooklyn, NY (US); Lukasz Czekaj, North Las Vegas, NV (US); Joseph Olivier, San Bruno, CA (US); Michael Prescott, Berkeley, CA (US); Jeong Eun Woo, Manhasset, NY (US); and Nicole Yin Chuen Lee Altman, Honolulu, HI (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Nov. 30, 2022, as Appl. No. 18/072,316.
Prior Publication US 2024/0177211 A1, May 30, 2024
Int. Cl. G06Q 30/06 (2023.01); G06Q 30/0601 (2023.01); G06Q 30/08 (2012.01)
CPC G06Q 30/0631 (2013.01) [G06Q 30/0629 (2013.01); G06Q 30/08 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method performed by one or more computer processors, the method comprising:
receiving an order comprising a set of items for a user;
identifying an ordered item from the set of items for suggesting a replacement item to the user;
determining a set of candidate replacement items for the ordered item;
determining a total score for each candidate replacement item in the set of candidate replacement items by:
determining a replacement score for the candidate replacement item indicating a likelihood that the candidate replacement item would be selected by the user as a replacement for the ordered item,
determining a source similarity score indicating a similarity between a first source of the ordered item and a second source of the candidate replacement item, wherein determining the source similarity score comprises:
accessing a first source embedding for the first source, wherein the first source embedding is generated by source embedding layers of a machine-learning model, wherein the machine-learning model comprises the source embedding layers and user embedding layers and is trained to predict user-source interactions based on user data and source feature data associated with a source, wherein the machine-learning model is trained based on training examples comprising source features associated with a source for input to the source embedding layers, user data describing a user for input to the user embedding layers, and a label indicating whether the user interacted with the source,
accessing a second source embedding for the second source, wherein the second source embedding is generated by the source embedding layers of the machine-learning model, and
computing a difference between the first source embedding and the second source embedding, and
combining the replacement score and the source similarity score to generate the total score;
selecting one or more candidate replacement items based on the total scores; and
transmitting information identifying the one or more selected replacement items for display to the user.