E-commerce Recommendation Engine
Personalized product recommendations using collaborative filtering and deep learning.
Duration
6 months
Team Size
4 developers
Industry
E-commerce
Category
AI/ML
E-commerce Recommendation Engine
A machine learning-powered recommendation system that delivers personalized product suggestions across the customer journey, driving significant revenue uplift.
The Challenge
An online retailer struggled with product discovery:
- Low conversion - Customers couldn't find relevant products
- Small basket sizes - Missing cross-sell opportunities
- Generic recommendations - Same products shown to everyone
- Cold start - No recommendations for new users
They needed intelligent, personalized discovery.
Our Approach
We built a multi-strategy recommendation engine that personalizes every touchpoint.
Recommendation Strategy
- Hybrid Approach - Collaborative + content-based filtering
- Real-Time Personalization - Adapt to browsing behavior
- Context Aware - Page type, time, device considerations
- Cold Start Handling - Smart defaults for new users
The Solution
Recommendation Types
- "Customers also bought" associations
- Personalized "for you" recommendations
- Similar product suggestions
- Recently viewed and saved items
Real-Time Personalization
- Session behavior tracking
- Click and view signals
- Cart analysis
- Purchase history
Model Architecture
- Collaborative filtering matrix
- Deep learning embeddings
- Content similarity scoring
- Ensemble ranking
A/B Testing
- Built-in experimentation
- Multi-armed bandits
- Statistical significance
- Revenue impact tracking
Technology Stack
| Layer | Technologies |
|---|---|
| ML Models | TensorFlow, Scikit-learn |
| Embedding Store | Redis, Faiss |
| Backend | Python, FastAPI |
| Database | PostgreSQL |
| Cloud | AWS (SageMaker, ElastiCache) |
| CDN | CloudFront |
Results & Impact
The engine drove significant growth:
- 25% higher AOV through cross-sell
- 15% better conversion with personalization
- 50ms response for real-time suggestions
- Real-time adaptation to user behavior
Technical Features
Embedding Strategy
- Product embeddings from behavior
- User preference vectors
- Visual similarity features
- Text embedding from descriptions
Caching & Performance
- Pre-computed recommendations
- Real-time score blending
- Edge caching
- Fallback strategies
Client Testimonial
"The recommendation engine is now responsible for 30% of our revenue. The real-time personalization makes every customer feel like the site was built for them."
— Chief Digital Officer, Online Retailer
Personalizing commerce? Contact us to discuss recommendation engine development.
Key Results
25% increase in average order value
15% improvement in conversion rate
50ms average response time
Real-time personalization
Technology Stack
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