  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.
