论文标题
沃尔玛电子商务中个性化对话语音模型的端到端ML系统
An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
论文作者
论文摘要
由于推荐系统的发展，在电子商务领域搜索和做出决定变得越来越容易。个性化和推荐系统已齐头并进，以帮助客户满足他们的购物需求并在此过程中改善他们的经验。随着购物对话平台的越来越多，建立个性化模型以处理大量数据并实时执行推断已经变得很重要。在这项工作中，我们提出了一种用于个性化对话语音贸易的端到端机器学习系统。我们包括用于隐式反馈，模型培训，更新评估以及实时推理引擎的组件。我们的系统将沃尔玛杂货客户的语音购物个性化，目前可通过Google Assistant，Siri和Google Home设备获得。
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.
