How an E-Commerce Product Recommendation Database Works: A Complete Guide
Boost sales and engagement with data-driven recommendations! Modern e-commerce platforms use databases to track user behavior, inventory, and purchase history—delivering personalized product suggestions that convert. This schema reveals how a high-performance system connects shoppers with the right items.
Schema Overview
This database powers an e-commerce recommendation engine, managing users, products, transactions, and AI-driven suggestions.
Core Components Explained
1. User Profiles & Preferences
Users {
user_id int pk
browsing_history json -- e.g., ["/electronics", "/books"]
purchase_frequency varchar(50) -- "High", "Medium", "Low"
}
- Purpose: Tracks tastes, past purchases, and activity patterns to tailor suggestions.
- Key Fields: browsing_history (for trend analysis), purchase_frequency (to prioritize promotions).
2. Product Catalog
Products {
product_id int pk
category varchar(100) -- e.g., "Electronics"
price_range varchar(50) -- "Budget", "Premium"
stock_status boolean
}
- AI Use Case: Products are tagged with metadata for matching (e.g., “similar items” or “frequently bought together”).
3. Real-Time User Activity
User_Activity {
session_id int
clicked_products int[] -- Array of product IDs
time_spent decimal(10,2)
}
- Why It Matters: Tracks clicks, cart additions, and dwell time to infer interest.
4. Recommendation Engine
Recommendations {
algorithm varchar(100) -- "Collaborative Filtering", "Content-Based"
rank int -- Priority order (1 = highest)
expires_at timestamp -- Freshness control
}
- How It Works: Combines user history, trending items, and inventory to rank suggestions.
- Key Metric: rank ensures top recommendations appear first.
5. Trending & Seasonal Items
Trending_Products {
trend_score decimal(10,2) -- 0–100 (popularity index)
seasonality varchar(50) -- "Holiday", "Summer"
}
- Business Impact: Surfaces high-demand items (e.g., “Back to School” deals).
Why This System Matters
- Personalization: Shoppers see relevant items, reducing bounce rates.
- Inventory Optimization: Promotes slow-moving stock via recommendations.
- Scalability: Handles millions of users and products efficiently.
Who Benefits
- Shoppers: Discover products aligned with their preferences.
- Retailers: Increase average order value (AOV) with cross-sell prompts.
- Marketing Teams: Leverage data for targeted campaigns.
Explore the Schema: Interactive E-Commerce Diagram
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