I jumped straight into graph traversal and mutual friends because that felt obvious, but then the interviewer kept pushing on scale and I realized I hadn't thought about how you'd actually compute this for hundreds of millions of users without it being a disaster.
Start by clarifying requirements and constraints (scale, latency, privacy, evaluation metrics) with the interviewer. Then outline a high-level architecture covering data sources, candidate generation, ranking, and serving, and dive into trade-offs for each component. Finally, discuss evaluation, iteration, and potential pitfalls.
Pro tip: Emphasize the importance of offline evaluation and online A/B testing, and mention how you would handle cold-start and privacy concerns—these are critical at Meta's scale.
Ask questions to understand scale (e.g., billions of users), latency requirements (e.g., <100ms), privacy constraints, and success metrics (e.g., CTR, mutual connections).
Outline the main components: data collection (user interactions, graph), candidate generation (e.g., friends-of-friends, embeddings), ranking (ML model), and serving (real-time API).
For each component, discuss specific techniques: e.g., for candidate generation, use graph traversal and approximate nearest neighbors; for ranking, use gradient boosted trees or deep neural networks with features like common friends, interactions, and profile similarity.
Discuss trade-offs between different approaches (e.g., batch vs. real-time, simple heuristics vs. complex models) and how to scale (sharding, caching, distributed processing).
Explain how to evaluate the system offline (precision/recall, AUC) and online (A/B tests, engagement metrics), and how to iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.