Spent the first few minutes just trying to scope it down because 'recommendation engine' could mean a hundred different things.
Start by clarifying the requirements and constraints, such as the scale, data sources, and business objectives. Then, outline a high-level architecture covering data collection, feature engineering, model training, and serving, and dive into trade-offs for each component. Finally, discuss evaluation metrics, online experimentation, and iteration strategies.
Pro tip: Emphasize the importance of a feedback loop and how you would handle cold start and scalability, as these are critical for a production recommendation system at Meta's scale.
Ask questions to understand the scope: what is being recommended (e.g., posts, videos, products), the scale (users, items), real-time vs. batch, and business goals (e.g., engagement, revenue).
Outline the end-to-end system: data ingestion, feature store, model training pipeline, model serving, and feedback collection. Mention offline vs. online components.
Discuss data sources (user interactions, item metadata, context), feature types (user, item, interaction), and techniques for feature engineering and embedding generation.
Compare candidate generation (e.g., matrix factorization, two-tower) and ranking models (e.g., deep learning, gradient boosted trees). Discuss trade-offs between accuracy, latency, and complexity.
Explain offline metrics (e.g., recall@k, NDCG) and online metrics (CTR, watch time). Describe A/B testing, feedback loops, and how to handle cold start and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.