I spent way too long on the obvious stuff like collaborative filtering and not enough time thinking about cold start or how you'd actually evaluate the feature.
Start by clarifying the product context and success metrics, then outline a high-level system architecture that covers data, modeling, and serving. Dive into the ML design choices, such as candidate generation and ranking, and discuss trade-offs between personalization, diversity, and business goals.
Pro tip: Emphasize how you would measure and optimize for long-term user satisfaction (e.g., retention) rather than just short-term clicks, and discuss how to handle cold-start and feedback loops in a production system.
Ask questions to understand the scope: What is 'Top Picks'? Is it a row on the homepage? What are the business objectives (e.g., engagement, retention)? Define offline and online metrics (e.g., CTR, watch time, diversity).
Sketch the end-to-end pipeline: data collection (user interactions, content metadata), feature engineering, model training, and serving. Mention components like candidate generation, ranking, and blending with business rules.
Propose a two-stage approach: candidate generation (e.g., collaborative filtering, embeddings) and ranking (e.g., deep neural networks with features). Discuss model choices, training data, and how to handle cold-start.
Explain offline evaluation (e.g., recall@k, NDCG) and online A/B testing. Discuss how to measure long-term effects and avoid feedback loops. Mention guardrail metrics (e.g., diversity, freshness).
Address trade-offs: personalization vs. diversity, relevance vs. freshness, latency vs. model complexity. Discuss scalability, real-time updates, and ethical considerations (e.g., filter bubbles).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.