100 Days of System Design Questions challenges

Design a recommendation system similar to Netflix's movie recommendation engine that provides personalized recommendations to users based on their viewing history, preferences, and behavior. The system should analyze user interactions with content, such as watching history, ratings, likes, and dislikes, to generate accurate and relevant recommendations. Consider factors such as recommendation algorithms, data collection, feature engineering, and model training in your design. Discuss how you would handle cold-start problems for new users and items, as well as data sparsity issues. Additionally, address scalability, real-time recommendation generation, and experimentation to continually improve recommendation quality. Provide insights into the technologies, machine learning models, and architectural patterns you would employ to build and deploy this recommendation system effectively.