AI Recommendation Engine for Global E-Commerce Marketplace

AI Recommendation Engine for Global E-Commerce Marketplace

The Challenge

ShopStream Global, an international e-commerce marketplace with over 12 million products and 8 million monthly active users, was underperforming on product discovery. Their existing recommendation system relied on simple collaborative filtering and manual merchandising rules, resulting in low click-through rates, poor cross-category discovery, and a growing gap with competitors who offered more personalized experiences.

Our Approach

We designed and built a modern recommendation engine that combines multiple ML techniques to deliver highly personalized product recommendations across every touchpoint of the shopping experience.

Data Foundation

We built a unified customer data platform that aggregates browsing behavior, purchase history, search queries, product interactions, and contextual signals (time, device, location) into a real-time feature store. The system processes over 50 million events daily.

Multi-Model Architecture

We developed a hybrid recommendation system with multiple specialized models:

1. Collaborative filtering: Deep learning-based matrix factorization for capturing user-product affinity patterns.

2. Content-based models: Product embeddings generated from images, descriptions, and attributes for capturing visual and semantic similarity.

3. Sequential models: Transformer-based session modeling that captures real-time browsing intent and predicts next-click behavior.

4. Contextual bandits: Online learning models that balance exploration of new products with exploitation of known preferences.

A ranking layer combines signals from all models with business rules (margin, inventory, promotions) to produce final recommendations.

Deployment Across Touchpoints

Recommendations are served across multiple surfaces:

• Homepage personalization.
• Product detail page ("Customers also viewed," "Complete the look").
• Search results re-ranking.
• Cart page cross-sells.
• Email campaigns and push notifications.
• Post-purchase follow-up.

Experimentation Platform

We built an A/B testing infrastructure that enables ShopStream to continuously experiment with recommendation algorithms, UI placements, and business rules, with automated statistical analysis and guardrail metrics.

Results

The new recommendation engine drove significant improvements across all key metrics within the first quarter. Revenue from recommendation-driven purchases increased by 22%, with particularly strong gains in cross-category discovery -- users exposed to the new system explored 40% more product categories.

Key Takeaways

• A hybrid multi-model approach outperforms any single recommendation technique.
• Real-time session context dramatically improves recommendation relevance.
• A robust experimentation platform is essential for continuous improvement.
• Recommendation quality directly impacts revenue, engagement, and customer lifetime value.