An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
This work addresses the problem of scaling personalized recommendations for conversational shopping platforms, though it appears incremental as it applies existing methods to a specific domain.
The paper tackles the challenge of building personalized conversational voice models for e-commerce by presenting an end-to-end ML system that personalizes voice shopping for Walmart Grocery customers, currently deployed on platforms like Google Assistant and Siri.
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.