Design a POI (Places of Interest) Recommendation System

Context

Design a global POI recommender for a mobile maps/feed product that suggests nearby places (e.g., restaurants, attractions) across surfaces such as a home feed, map viewport, and search results. The system must support personalization, freshness, and high scale while meeting strict latency targets.

Specify

(a) Product goals and key requirements:

• Personalization: individualized to user tastes, intents, and context
• Freshness: reflect open/closed status, trending, events, new places
• Latency: responsive on mobile; include p50/p95 budgets
• Scale: global POIs, high QPS, multi-region deployment

(b) Data and features:

• Data sources: map metadata, reviews, check-ins, GPS pings, events
• Labeling strategy: define positives/negatives, counterfactual logging, debias for position/exposure
• Feature sets: user, POI, context, interaction, geographic features

(c) Architecture:

• Two-stage retrieval: candidate generation (embeddings/ANN) and ranking (GBDT or deep)
• Re-ranker for diversity and novelty

(d) Training and serving:

• Batch + streaming updates, feature store, backfills
• Online serving: feature retrieval, caching, latency budgets, fallbacks

(e) Exploration/exploitation:

• Strategy (e.g., bandits, epsilon-greedy) for cold start and long-term learning

(f) Evaluation plan:

• Offline metrics (AUC, NDCG, coverage)
• Online A/B metrics (CTR, save/visit rate, dwell)

(g) Trust & safety:

• Privacy, abuse/spam prevention, geo-specific fairness considerations