I went straight to collaborative filtering and probably spent too long there before they nudged me toward the product side of things.
Start by clarifying the business objectives and constraints, then outline a high-level system design that balances user personalization, scalability, and real-time responsiveness. Walk through the key components, data flow, and trade-offs, emphasizing how you would measure success and iterate based on metrics.
Pro tip: Anchor your design in Amazon's leadership principles, such as Customer Obsession and Dive Deep, by explicitly linking each design choice to customer impact and long-term value. Also, discuss how you would handle cold-start and scalability from day one, as Amazon operates at massive scale.
Ask questions to understand the platform's scale, user base, business objectives (e.g., increase conversion, cross-sell), and constraints (latency, budget). Define success metrics like CTR, conversion rate, and revenue lift.
Describe the main components: data ingestion (user events, product catalog), data storage (user profiles, item features), model training (collaborative filtering, content-based, hybrid), and serving layer (real-time API). Mention batch vs. real-time processing.
Explain the recommendation algorithms you'd use (e.g., matrix factorization, deep learning, contextual bandits) and the features (user behavior, item attributes, context). Discuss how to handle cold-start and diversity.
Discuss trade-offs between accuracy and latency, personalization and privacy, and offline vs. online evaluation. Explain how you'd scale the system (e.g., distributed training, caching, A/B testing infrastructure).
Propose offline metrics (precision@k, recall) and online metrics (A/B tests, engagement). Describe how you'd monitor, learn, and iterate, including feedback loops and model retraining cadence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.