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🛒 How a Large Online Retailer Built a Recommendation Engine

A detailed case study of how a major e-commerce marketplace used collaborative filtering, content-based recommendations and personalisation to lift average order value and conversion.

AI & Machine Learning3DAdvanced60 FPS

A 3D map of a product embedding space where a shopper's position and a blend of collaborative-filtering and content-based signals determine which items get recommended.

🔬 What It Demonstrates

Products cluster two ways: by category (content-based) or by real co-purchase patterns (collaborative filtering). Blending the two changes which items sit nearest the shopper — and therefore which get recommended.

🎮 How to Use

Pick a shopper persona, then adjust the collaborative-filtering weight, the number of recommendations (k), and diversity. Watch the highlighted, connected products change along with predicted AOV and conversion lift.

💡 Did You Know?

Hybrid recommenders that mix collaborative and content-based signals typically outperform either approach alone, and are especially important for new shoppers with little history — the "cold start" problem.

⚙ Under the hood

A detailed case study of how a major e-commerce marketplace used collaborative filtering, content-based recommendations and personalisation to lift average order value and conversion.

machine learningrecommendation systemsdata analysisecommercealgorithmspersonalizationcollaborative filteringThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install