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 \ours. 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.

现实世界中的产品组合选择是物理零售商面临的一项关键挑战，通过有效地与购物者的喜好相匹配，可以增加销售并减少缺货。为解决这些挑战，我们引入了一个实时推荐系统，利用 3D 计算机视觉和感知技术，以及借助贝叶斯奖励模型、空间聚类和基于图的候选生成算法，以适应异质的消费者喜好和解决组合搜索问题。通过在真实店铺进行多次测试和观察研究，我们展示了销售量分别提高了 35% 和 27%。