Incrementally updating embeddings for use in a machine learning model by accounting for effects of the updated embeddings on the machine learning model

US12423722B2 · kind B2 · utility

Assignee

• Maplebear Inc. · US

Inventors

• Chuanwei Ruan · Sunnyvale, US
• Ramasubramanian Balasubramanian · Jersey City, US
• Peng Qi · Jinan, CN

Key dates

• Filing date: Jul 9, 2024
• Grant date: Sep 23, 2025
• Expiry date: Jul 9, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0631
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.