How to Answer Designing a Content Recommendation System in a ByteDance Interview

To design a scalable content recommendation system for TikTok, I would begin by identifying the key requirements such as real-time recommendations, personalization, and scalability to handle millions of users. I would leverage user interaction data, such as likes, shares, and watch time, along with content metadata, to build a robust dataset. For architecture, I would propose a microservices approach, using a combination of NoSQL databases for flexible data storage and Redis for caching frequently accessed content. To generate recommendations, I would implement a hybrid algorithm that combines collaborative filtering and content-based filtering, allowing for both personalized and trending recommendations. This approach ensures that users receive relevant content while the system can scale efficiently as usage grows.