I jumped straight into the ML model before nailing down the problem scope, which I think hurt me.
Start by clarifying the product goals and constraints (e.g., user engagement, diversity, freshness) and the scale (billions of users, millions of articles). Then outline a multi-stage ranking system: candidate generation, ranking, and re-ranking, with personalization via user embeddings and contextual features. Discuss trade-offs between relevance, diversity, and latency, and how you would evaluate and iterate.
Pro tip: Emphasize the importance of balancing short-term engagement metrics with long-term user satisfaction and ecosystem health, and propose a multi-objective optimization framework with guardrails. This shows product maturity and avoids the trap of over-optimizing for clicks.
Ask questions to understand the product goals (e.g., engagement, diversity, freshness), scale (users, items), latency requirements, and available data. Define success metrics and guardrails.
Outline a multi-stage pipeline: candidate generation (e.g., from followed entities, trending, embeddings), ranking (e.g., deep neural network with user/item features), and re-ranking (e.g., diversity, freshness, business rules).
Explain how to represent users and items (e.g., embeddings, features), the model architecture (e.g., two-tower for retrieval, DNN for ranking), and how to incorporate real-time signals.
Discuss trade-offs between relevance, diversity, latency, and computational cost. Describe offline evaluation (e.g., AUC, NDCG) and online A/B testing with guardrail metrics.
Explain how to handle cold start, feedback loops, and model updates. Mention infrastructure considerations like distributed training and serving at scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.