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

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 th...

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Bibliographic Details
Published in:arXiv.org 2020-11
Main Authors: Rahul Radhakrishnan Iyer, Kanumala, Praveenkumar, Guo, Stephen, Kannan Achan
Format: Article
Language:English
Subjects:
Customers
Customization
Electronic commerce
Inference
Machine learning
Real time
Recommender systems
Voice
Online Access:Get full text
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Summary: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.
ISSN:2331-8422