This one sprawls fast if you're not careful.
Start by clarifying the problem scope and business objectives, then outline a two-stage recommendation architecture (candidate generation + ranking) with appropriate models. Address cold-start with hybrid and content-based strategies, and describe a comprehensive evaluation plan that includes offline metrics, online A/B tests, and long-term business metrics.
Pro tip: Emphasize the importance of aligning offline metrics with online business goals and discuss how you would handle the exploration-exploitation trade-off in production to avoid feedback loops.
Ask questions to understand the product context, scale, and key business objectives (e.g., user engagement, restaurant partner satisfaction). Define what a successful recommendation looks like.
Propose a two-stage system: candidate generation (e.g., matrix factorization, two-tower neural networks) to narrow down from millions to hundreds, followed by ranking (e.g., gradient boosted decision trees, deep learning) to order the final list.
For new users, use onboarding preferences and contextual bandits; for new restaurants, leverage content-based features (cuisine, location, price) and explore via multi-armed bandits. Consider hybrid models that combine collaborative and content signals.
Use metrics like Recall@K, NDCG, MAP, and AUC on held-out data. Perform temporal validation to mimic production. Also, conduct offline counterfactual evaluation to estimate online performance.
Run A/B tests measuring click-through rate, conversion rate, and user retention. Monitor guardrail metrics (e.g., latency, diversity). Use interleaving and multi-armed bandits for faster iteration. Analyze long-term effects via holdout groups.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.