Summary of Personalized Product Assortment with Real-time 3d Perception and Bayesian Payoff Estimation, by Porter Jenkins et al.
Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation
by Porter Jenkins, Michael Selander, J. Stockton Jenkins, Andrew Merrill, Kyle Armstrong
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A real-time recommendation system, called EdgeRec3D, is introduced to tackle the challenge of product assortment selection for physical retailers. The system utilizes recent advances in 3D computer vision for perception and automatic sales estimation, running on the edge of the network to facilitate real-time reward signals. It also employs a Bayesian payoff model to account for noisy estimates from LIDAR data, spatial clustering to adapt to heterogeneous consumer preferences, and a graph-based candidate generation algorithm to address the combinatorial search problem. The system is tested in real-world stores across two A/B tests with beverage products, demonstrating a 35% and 27% increase in sales respectively. Additionally, an observational study shows a 9.4% increase in sales over a period of 28 weeks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new system helps physical stores decide which products to have in stock. It uses special computer vision to understand what people like to buy and can make good choices fast. This system is called EdgeRec3D. It can help stores sell more things by picking the right products. The system works well and made a lot of sales go up. |
Keywords
» Artificial intelligence » Clustering
