Implement collaborative filtering algorithm for recommendations

Objective

Build a collaborative filtering algorithm to generate personalized content recommendations based on user behavior patterns.

Technical Approach

• Use matrix factorization (SVD) for user-item interactions
• Implement k-nearest neighbors for similarity matching
• Build real-time scoring engine

Acceptance Criteria

• Algorithm achieves >70% accuracy on test dataset
• Recommendations generated in <100ms
• Handles 100k+ users efficiently

Dependencies

• Requires user behavior tracking data
• Needs distributed computing infrastructure

Epic: &9 AI Recommendation Engine