Product Recommendation Engine Challenge

🎯 Challenge Overview

Build a product recommendation engine that suggests relevant items to users based on their browsing history, purchase patterns, and similar user behaviors. This challenge is inspired by the recommendation systems used by major e-commerce platforms like Amazon, where showing the right product at the right time can significantly impact sales and user experience.

🏢 Real-World Context

E-commerce giants like Amazon generate over 35% of their revenue from recommendation systems. Your task is to build a simplified version of such a system that can:

• Analyze user behavior patterns
• Calculate product similarity scores
• Generate personalized recommendations
• Handle cold start problems for new users

📋 Requirements

Core Features

1. Collaborative Filtering: Recommend products based on similar users' preferences
2. Content-Based Filtering: Suggest items similar to what users have previously engaged with
3. Hybrid Approach: Combine both methods for better accuracy
4. Cold Start Handling: Provide recommendations for new users with no history

Technical Specifications

• Process a dataset of 10,000+ user interactions
• Achieve recommendation accuracy of >70% on test data
• Handle real-time recommendation requests in <200ms
• Support both explicit (ratings) and implicit (views, clicks) feedback

🛠 Implementation Guide

Phase 1: Data Processing

# Expected data structure
user_interactions = {
    'user_id': int,
    'product_id': int, 
    'rating': float,  # 1-5 or implicit score
    'timestamp': datetime,
    'action_type': str  # 'view', 'purchase', 'rating'
}

Phase 2: Algorithm Implementation

• Implement user-item collaborative filtering using cosine similarity
• Build content-based filtering using product features
• Create a hybrid model that weights both approaches

Phase 3: API Development

Build REST endpoints for:

• GET /recommendations/{user_id} - Get personalized recommendations
• POST /feedback - Record user interactions
• GET /similar/{product_id} - Find similar products

📊 Success Metrics

• Precision@10: >70% of top 10 recommendations should be relevant
• Coverage: System should recommend at least 80% of catalog items
• Diversity: Recommendations should span multiple product categories
• Response Time: <200ms for recommendation generation

🎁 Bonus Challenges

1. Real-time Learning: Update recommendations as users interact
2. Explainable AI: Provide reasons for each recommendation
3. A/B Testing Framework: Compare different recommendation strategies
4. Trending Products: Incorporate popularity and seasonality factors

📚 Learning Outcomes

After completing this challenge, you'll understand:

• How recommendation systems work at scale
• Trade-offs between different filtering approaches
• Handling sparse data and cold start problems
• Performance optimization for real-time systems
• Evaluation metrics for recommendation quality

🔗 Helpful Resources

• Surprise Library for collaborative filtering
• scikit-learn for content-based filtering
• FastAPI for building the recommendation API
• Apache Kafka for real-time data streaming (bonus)

🚀 Getting Started

1. Download the provided dataset (user interactions + product catalog)
2. Explore the data to understand user behavior patterns
3. Implement basic collaborative filtering algorithm
4. Add content-based filtering for product features
5. Build the API endpoints and test with sample users
6. Evaluate your system's performance and iterate

Ready to build the next Amazon recommendation engine? Start coding and discover how data-driven personalization can transform user experiences!