Adaptive Recommender System Architecture

Architect a next-generation recommender system that combines collaborative filtering, content-based approaches, and contextual bandits. Implement a multi-armed bandit algorithm with Thompson sampling to balance exploration and exploitation dynamically. Design a feature engineering pipeline that can incorporate user behavior, temporal patterns, and external contextual signals. Include mechanisms for real-time model updating, personalization at scale, and interpretable recommendation explanations.