Product assortment selection is a critical challenge facing physical retailers. Effectively aligning inventory with the preferences of shoppers can increase sales and decrease out-of-stocks. However, in real-world settings the problem is challenging due to the combinatorial explosion of product assortment possibilities. Consumer preferences are typically heterogeneous across space and time, making inventory-preference alignment challenging. Additionally, existing strategies rely on syndicated data, which tends to be aggregated, low resolution, and suffer from high latency. To solve these challenges, we introduce a real-time recommendation system, which we call EdgeRec3D. Our system utilizes recent advances in 3D computer vision for perception and automatic, fine grained sales estimation. These perceptual components run on the edge of the network and facilitate real-time reward signals. Additionally, we develop a Bayesian payoff model to account for noisy estimates from 3D LIDAR data. We rely on spatial clustering to allow the system to adapt to heterogeneous consumer preferences, and a graph-based candidate generation algorithm to address the combinatorial search problem. We test our system in real-world stores across two, 6-8 week A/B tests with beverage products and demonstrate a 35% and 27% increase in sales respectively. Finally, we monitor the deployed system for a period of 28 weeks with an observational study and show a 9.4% increase in sales.

翻译：商品组合选择是实体零售商面临的关键挑战。有效匹配库存与顾客偏好能够提升销售额并减少缺货现象。然而在实际场景中，由于商品组合可能性的组合爆炸问题，该问题极具挑战性。消费者偏好通常具有时空异质性，使得库存-偏好匹配尤为困难。此外，现有策略依赖聚合化、低分辨率且高延迟的第三方数据。为解决这些挑战，我们提出了名为EdgeRec3D的实时推荐系统。该系统利用三维计算机视觉领域的最新进展进行环境感知与自动化细粒度销量估计。这些感知组件部署在网络边缘，可生成实时奖励信号。同时，我们开发了贝叶斯收益模型以处理三维激光雷达数据中的噪声估计。通过空间聚类技术使系统适应异质性消费者偏好，并采用基于图的候选生成算法应对组合搜索问题。我们在实体商店对饮料产品进行了两次为期6-8周的A/B测试，分别实现销售额提升35%和27%。最后通过为期28周的观察性研究监测部署系统，展示出9.4%的销售额增长。