Start by clarifying the product goals and user experience, then walk through the ML lifecycle from data to serving, highlighting trade-offs at each stage. Emphasize Meta-specific considerations like social signals, privacy, and large-scale experimentation.
Pro tip: Anchor your design around measurable business metrics (e.g., CTR, dwell time) and explicitly discuss how you'd A/B test and iterate, showing you think like an owner, not just a modeler.
Define the product objectives (e.g., increase user engagement, discovery) and constraints (latency, privacy, scale). Identify key stakeholders and success metrics.
List data sources: user check-ins, social graph, place metadata, reviews, and contextual signals. Discuss how to engineer features like user embeddings, place embeddings, and real-time context.
Propose a two-tower retrieval model for candidate generation and a ranking model (e.g., GBDT or deep NN) for final scoring. Describe offline training with negative sampling and online fine-tuning.
Outline a low-latency serving stack with caching, precomputed embeddings, and real-time feature fetching. Discuss exploration strategies like epsilon-greedy or Thompson sampling to balance exploitation and discovery.
Define offline metrics (recall@k, NDCG) and online A/B tests with guardrail metrics. Address privacy: differential privacy, federated learning, and user consent for location data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.