Design place-of-interest ML system

Design a machine learning system that recommends places of interest (POIs) to users. Specify: (a) product goals and key requirements (personalization, freshness, latency, scale), (b) data sources (map metadata, reviews, check-ins, GPS pings, events), labeling strategy (defining positives, counterfactual logging, debiasing for position/exposure), and feature sets (user, POI, context, interaction, geographic features), (c) a two-stage architecture with candidate generation (e.g., embeddings/ANN) and ranking (e.g., GBDT or deep models), plus a re-ranker for diversity/novelty, (d) training pipeline (batch + streaming updates, feature store, backfills) and online serving (feature retrieval, caching, latency budgets, fallbacks), (e) exploration/exploitation strategy (bandits or epsilon-greedy) for cold start and long-term learning, (f) evaluation plan with offline metrics (AUC, NDCG, coverage) and online A/B metrics (CTR, save/visit rate, dwell), and (g) privacy, abuse/spam prevention, and geo-specific fairness considerations.