Project · RANKDECODER
RankDecoder
Explainable ranking intelligence for AI-powered commerce.
Problem
AI-powered shopping systems increasingly influence product discovery and ranking. Understanding why a product receives a particular position can become critical for commerce.
Approach
RankDecoder uses hierarchical ridge regression to analyze the influence of multiple ranking factors.
Model
11 factors across 10 product categories.
Performance
95% R².
Data Processing
12 million records processed in 36 seconds on a laptop environment.
Scenarios
The system includes scenario analysis across 19 business and market situations, including inflation, recession, supply shocks and black-swan scenarios.
Technology
Explainable quantitative modeling.
Purpose
To understand ranking behavior and support explainable decision-making in AI-powered commerce.