Patent ID: 11935109
Assignee: MAPLEBEAR INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. A system comprising:
a processor;
a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to:
obtain, at an online concierge system, a plurality of recipes, wherein each recipe includes items and instructions for combining the included items;
obtain, at an online concierge system, training data from prior interactions by users with recipes, the training data including a plurality of examples, each example comprising a user, a recipe, and a label indicating whether the user performed a specific interaction with the recipe;
initialize a plurality of layers of a machine learning recommendation model, the recommendation model being configured to receive attributes of the recipe and characteristics of the user;
for each of a plurality of examples of the training data:
generate a user embedding for a user of an example from application of a user model comprising a first plurality of layers of the recommendation model to characteristics of the user of the example;
generate a recipe embedding for a recipe of the example from application of a recipe model comprising a second plurality of layers of the recommendation model to attributes of the recipe of the example;
generate a measure of similarity between the user embedding for the user of the example and the recipe embedding for the recipe of the example, the similarity measured in a latent space including the user embedding and the recipe embedding;
generate an error term based on a difference between the measure of similarity between the user embedding for the user of the example and the recipe embedding for the recipe of the example and a label of the example;
backpropagate the error term through the user model and through the recipe model to update a set of parameters of the recommendation model; and
stop the backpropagation after one or more criteria are satisfied; and

obtain a plurality of classification examples, each classification example including a classified user, a classified recipe, a classification label indicating a dietary preference type of the classified user, and a classification label indicating a dietary preference type of the classified recipe; and
for each of the plurality of classification examples:
generate a user embedding for a classified user of a classification example from application of the user model to characteristics of the classified user of the classification example;
generate a recipe embedding for a classified recipe of the classification example from application of the recipe model to attributes of the classified recipe of the classification example;
generate a predicted dietary type for the classified user and a predicted dietary type for the classified recipe output by a set of classification layers receiving the user embedding for the classified user of the classification example and the recipe embedding for the classified recipe of the classification example;
generate a classification error term based on a difference between the predicted dietary type for the classified user and the classification label indicating the dietary preference type of the classified user or based on a difference between the predicted dietary type for the classified recipe and the classification label indicating the dietary preference type of the classified recipe;
backpropagate the classification error term through the user model and through the recipe model to update the set of parameters of the recommendation model, resulting in an updated set of parameters of the recommendation model; and
stop the backpropagation of the classification error term after one or more criteria are satisfied.