The rough part wasn't the question itself, it was that the interviewer clearly wasn't coming from a recommendations background.
Start by clarifying the business goal and constraints, then outline a two-stage recommendation architecture (candidate generation and ranking) that balances personalization, real-time context, and business objectives. Emphasize how you would evaluate the system offline and online, and discuss trade-offs between relevance, diversity, and latency.
Pro tip: Show awareness of Uber Eats' unique marketplace dynamics—like delivery time, courier availability, and restaurant prep time—and how they affect ranking. Mention that you'd optimize for long-term user retention and merchant fairness, not just short-term CTR.
Ask questions to understand the goal: is it to increase orders, user engagement, or merchant exposure? Identify key constraints like latency, scalability, and cold-start users.
Propose a two-stage system: candidate generation (e.g., collaborative filtering, content-based, or geographic retrieval) followed by ranking (e.g., a deep learning model with features like user history, restaurant attributes, and real-time context).
Explain how to integrate delivery time estimates, courier availability, promotions, and diversity constraints into the ranking model to balance user satisfaction and marketplace health.
Describe offline metrics (e.g., NDCG, recall@k) and online A/B tests (e.g., CTR, conversion, retention). Mention the need for guardrail metrics like delivery time and merchant fairness.
Discuss trade-offs between personalization and exploration, latency and model complexity, and short-term vs. long-term objectives. Outline a plan for continuous improvement and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.