Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation

TL;DR

This paper presents EdgeRec3D, a real-time, edge-based 3D perception and Bayesian modeling system for personalized product assortment, significantly improving sales in retail through adaptive, perception-driven recommendations.

Contribution

The paper introduces a novel real-time recommendation system combining 3D computer vision and Bayesian payoff estimation for dynamic retail assortment optimization.

Findings

Achieved 35% and 27% sales increases in real-world store A/B tests.

Demonstrated a 9.4% sales increase in a 28-week observational study.

Utilized edge computing and spatial clustering for adaptive, personalized recommendations.

Abstract

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.