Advanced Probabilistic Recommendation Engine Architecture

Construct a sophisticated recommendation system using probabilistic graphical models and advanced collaborative filtering techniques. Develop a framework that combines matrix factorization, Bayesian inference, and contextual bandits to generate personalized recommendations. Include mechanisms for handling cold start problems, incorporating explicit and implicit user feedback, and maintaining real-time recommendation performance.