Recommendation engine for a global retailer

The Challenge

A global e-commerce retailer with millions of products was struggling to help customers discover relevant items. Their generic "you might also like" recommendations were based on simple category matching, resulting in low engagement and missed revenue opportunities.

Key challenges included:

• Low click-through rates on product recommendations (under 5%)
• Stagnant average order values
• Difficulty surfacing long-tail inventory
• One-size-fits-all approach that ignored user preferences
• Limited personalization capabilities

Our Solution

We built a sophisticated recommendation engine that combines collaborative filtering, content-based filtering, and deep learning to deliver hyper-personalized product suggestions.

1. Multi-Model Recommendation System

Our system uses multiple ML models working in concert: collaborative filtering for "customers like you" recommendations, content-based filtering for similar products, and a neural network for complex pattern recognition across user behavior, product attributes, and contextual signals.

2. Real-Time Personalization

The engine processes user behavior in real-time, adapting recommendations based on current browsing session, purchase history, seasonal trends, and even time of day. Each user sees a unique, dynamically generated product feed.

3. A/B Testing Framework

We implemented a robust experimentation platform that continuously tests different recommendation strategies, allowing the system to learn what works best for different user segments and product categories.

The Results

The recommendation engine delivered significant business impact:

• 18% increase in average order value
• 24% boost in repeat purchase rate
• 35% click-through rate on recommendations (up from 4.8%)
• 40% improvement in long-tail product discovery
• $12M additional revenue in the first year
• 28% reduction in cart abandonment

Technologies Used

Python, TensorFlow, PyTorch, Apache Spark, Redis, Elasticsearch, React, Next.js, AWS (SageMaker, Lambda, DynamoDB), Snowflake