System and method for personalized item recommendations through large-scale deep-embedding architecture

US11288730B2 · kind B2 · utility

Assignee

• Walmart Apollo, LLC · US

Inventors

• Yokila Arora · Sunnyvale, US
• Aditya Mantha · Sunnyvale, US
• Shubham Gupta · Jaipur, IN
• Praveenkumar Kanumala · Newark, US
• Stephen Dean Guo · Saratoga, US
• Kannan Achan · Saratoga, US

Key dates

• Filing date: Jan 30, 2020
• Grant date: Mar 29, 2022
• Expiry date: Apr 5, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0633
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

A method including receiving a basket including basket items selected by a user from an item catalog. The method also can include grouping the basket items of the basket into categories based on a respective item category of each of the basket items. The method additionally can include randomly sampling a respective anchor item from each of the categories. The method further can include generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user. The two sets of trained item embeddings and the trained user embeddings can be trained using a triple embeddings model with triplets. The triplets each can include a respective first user of users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method additionally can include building a list of personalized recomme…