Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can help boost the quality of shopping experience by enabling provision of more relevant, explainable, and diversified recommendations. However, such high level shopping intent has been overlooked in the industry due to practical challenges. In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations. We develop a novel technique that automatically identifies various high level goals being pursued by the Amazon customers, such as "go camping", and "preparing for a beach party". Our solution is in a scalable fashion (in 14 languages across 21 countries). Then a deep learning model maps each customer's online behavior, e.g. product search and individual item engagements, into a subset of high level shopping intents. Finally, a realtime ranker considers both the identified intents as well as the granular engagements to present personalized intent-aware recommendations. Extensive offline analysis ensures accuracy and relevance of the new recommendations and we further observe an 10% improvement in the business metrics. This system is currently serving online traffic at amazon.com, powering several production features, driving significant business impacts

翻译：理解顾客的高层次购物意图（例如，他们想去露营或举办生日派对的愿望）对于电子商务平台至关重要；这有助于通过提供更相关、可解释且多样化的推荐来提升购物体验的质量。然而，由于实际挑战，这种高层次购物意图在行业中一直被忽视。本文介绍了亚马逊的新系统，该系统明确识别并利用每位顾客的高层次购物意图来实现个性化推荐。我们开发了一种新技术，能够自动识别亚马逊顾客正在追求的各种高层次目标，例如“去露营”和“准备海滩派对”。我们的解决方案具有可扩展性（覆盖21个国家的14种语言）。随后，一个深度学习模型将每位顾客的在线行为（例如，产品搜索和单个商品互动）映射到高层次购物意图的子集。最后，一个实时排名器同时考虑识别出的意图以及细粒度互动，以呈现具有意图感知的个性化推荐。广泛的离线分析确保了新推荐的准确性和相关性，我们进一步观察到业务指标提升了10%。该系统目前在亚马逊官网上处理在线流量，支持多项生产功能，并带来了显著的业务影响。