Personalized item recommendations through large-scale deep-embedding architecture with real-time inferencing

US11113744B2 · kind B2 · utility

Assignee

• Walmart Apollo, LLC · US

Inventors

• Aditya Mantha · Sunnyvale, US
• Yokila Arora · 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: Sep 7, 2021
• Expiry date: Apr 12, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0641
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

A method including training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each can include a respective first user of the 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 also can include generating an approximate nearest neighbor index for the two sets of item embeddings. The method additionally can include receiving a basket including basket items selected by a user from the item catalog. The method further 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 lookup call to the approximate nearest neighbor index using a query v…