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

本文介绍了亚马逊的新系统，使用深度学习技术，将用户的在线行为映射到各种高水平购物意图，以个性化推荐，提高了推荐准确性和业务指标的 10％。