Start by clarifying requirements and scale, then design a multi-stage architecture (candidate generation, ranking, re-ranking) with a real-time feature store and streaming updates. Focus on trade-offs between latency, freshness, and accuracy, and explain how you'd evaluate and iterate on the system.
Pro tip: Emphasize the importance of a feature store that serves both batch and real-time features consistently, and discuss how you'd handle cold-start and popularity bias in a production system.
Ask questions to understand user scale, item catalog size, latency SLA, update frequency, and personalization goals. Define metrics like CTR, engagement, and diversity.
Propose a multi-stage pipeline: candidate generation (e.g., ANN, collaborative filtering), ranking (e.g., DNN), and re-ranking (business rules, diversity). Include data flow from user interactions to model updates.
Design a low-latency serving layer with caching, precomputed embeddings, and a feature store that supports online/offline consistency. Discuss streaming updates via Kafka and model refresh strategies.
Discuss trade-offs: latency vs. model complexity, freshness vs. stability, and cost vs. performance. Explain sharding, replication, and fallback mechanisms for high availability.
Describe offline evaluation (A/B testing, counterfactual logging) and online metrics. Mention monitoring, bias detection, and continuous improvement loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.