An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce

TL;DR

This paper presents an end-to-end machine learning system designed for personalized conversational voice shopping in e-commerce, enabling real-time inference and personalization at scale for Walmart Grocery customers across multiple voice platforms.

Contribution

It introduces a comprehensive system integrating feedback, training, evaluation, and inference components specifically for personalized voice commerce.

Findings

System successfully personalizes voice shopping for Walmart customers.

Real-time inference enables seamless user experience.

System is deployed on major voice platforms like Google Assistant and Siri.

Abstract

Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems. Personalization and recommender systems have gone hand-in-hand to help customers fulfill their shopping needs and improve their experiences in the process. With the growing adoption of conversational platforms for shopping, it has become important to build personalized models at scale to handle the large influx of data and perform inference in real-time. In this work, we present an end-to-end machine learning system for personalized conversational voice commerce. We include components for implicit feedback to the model, model training, evaluation on update, and a real-time inference engine. Our system personalizes voice shopping for Walmart Grocery customers and is currently available via Google Assistant, Siri and Google Home devices.