E-commerce Recommendation Engine
Client: Online retailer - 2M visitors/month
The project
Design of a hybrid recommendation engine (collaborative + content-based) to personalize the shopping experience of 2 million monthly visitors. Real-time recommendations based on browsing behavior.
Architecture & implementation
Real-time architecture with Apache Kafka and Redis serving recommendations in under 50 ms. The model is continuously updated with user interactions and retrained every night.
Monitoring & adoption
Rigorous A/B testing over 6 months to validate business impact: +28% click-through rate on recommendations, +19% average order value and +23% overall conversion rate on product pages.
Results & production rollout
Admin interface allowing the marketing team to manage recommendation rules, exclude specific products and measure the impact of each campaign in real time.
Screenshots
