The scope is huge and that's kind of the problem.
Start by clarifying the product context and requirements, then outline the ML system architecture from data to serving, and finally discuss trade-offs and evaluation. Focus on how you would balance relevance, diversity, and business objectives while handling cold-start and scalability.
Pro tip: Emphasize the importance of defining clear offline and online metrics that align with user satisfaction and business goals, and discuss how you would iterate using A/B testing. Show awareness of the two-sided nature of recommendations (users and places) and how to handle feedback loops.
Ask questions to understand the product (e.g., Maps vs. Local Discovery), user goals, scale, and constraints. Define success metrics and key challenges like cold-start, real-time updates, and privacy.
Identify data sources: user interactions (searches, clicks, visits), place attributes (category, location, reviews), and contextual signals (time, weather). Discuss feature engineering for user, place, and context.
Propose a two-stage architecture: candidate generation (e.g., collaborative filtering, embedding-based retrieval) and ranking (e.g., gradient boosted trees or deep neural networks). Mention handling of cold-start via content-based methods.
Outline the serving pipeline: offline training, near-real-time updates, and online inference with low latency. Discuss scalability, caching, and fallback strategies.
Define offline metrics (e.g., recall@k, NDCG) and online metrics (CTR, conversion, user retention). Describe A/B testing, counterfactual evaluation, and how to monitor for bias and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.