This question evaluates proficiency in designing scalable, low-latency ML-powered recommendation system architectures, covering real-time data ingestion and streaming feature computation, real-time feature stores, service contracts for candidate generation and ranking, caching and freshness, personalization state, scalability, experimentation, failure recovery, and operational concerns like monitoring, privacy, and security. It is commonly asked to gauge an engineer's ability to reason about trade-offs between latency, freshness, consistency, and cost for global high-throughput platforms; category: ML System Design (recommender systems), domain: systems architecture and operations, level of abstraction: practical application and architectural design rather than model internals.
You are designing a real‑time recommendation system for a large gaming platform with millions of games and tens of millions of users. The focus is on system architecture and operations, not the internals of ML models.
Describe the following aspects clearly:
Assume:
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