Event-Driven Content Recommendation Machine Learning Pipeline

Develop a sophisticated machine learning recommendation pipeline that processes real-time user interaction data from a media platform. Create a system that can dynamically update recommendation models using streaming event data, implement feature engineering for personalization, and support A/B testing of recommendation algorithms. Include mechanisms for handling cold-start problems, privacy-preserving feature extraction, and scalable model training approaches.