US 12,762,604 B1Granted

Product attribute extraction from product ingredients

AssigneeAmazon Technologies, Inc.

InventorsSiliang Liu, Rahul Suresh, Amin Banitalebi Dehkordi

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Abstract

Systems and methods are described for a product recommendation system for automatically extracting ingredient-related attributes for a product based on the product's ingredients in order to generate product recommendations for a user. The systems and methods may use a model to determine the attributes from the product ingredients. The primary input into the model may include a product ingredient list, and optionally a product title. The model may identify product attributes, such as target skin types and concerns, for a product based on the product's ingredients. The product recommendation system may then match these attributes with user preferences and then generate product recommendations tailored to match the user's preferences based on the matching.

A system comprising:

a memory storing computer-executable instructions;
and
a processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, cause the processor to at least:
train, using a dataset comprising (a) product ingredients for each of a plurality of sample products and (b) a set of ingredient-derived attributes associated with each sample product of the plurality of sample products, a model to generate, from product ingredients of an input product, an embedding derived from the product ingredients of the input product and to predict, based at least in part on the embedding, which of a plurality of product attributes are associated with the input product;
for each individual product of a plurality of products, wherein the plurality of products are skincare products and are different than the plurality of sample products included in the dataset:
receive a list of ingredients associated with the individual product;
generate, using the model, an embedding derived from the list of ingredients associated with the individual product;
determine, using the model and based at least in part on the embedding derived from the list of ingredients associated with the individual product, at least one product attribute of the plurality of product attributes is associated with the individual product;
store the at least one product attribute associated with the individual product as one or more learned attributes associated with the individual product;
access a user profile associated with a user, wherein the user profile comprises one or more user attributes indicating target skincare preferences for the user;
determine that one or more learned attributes associated with at least one product of the plurality of products match at least one user attribute of the one or more user attributes;
and
generate personalized product recommendations for the user that include the at least one product matching the at least one user attribute.

10 more elements

Abstract

Claims20