Design nearby place recommendations
Company: Meta
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: hard
Interview Round: Onsite
Design an app that recommends nearby places to a user in real time. Define objectives and success metrics; describe data sources (maps/POIs, user behavior), candidate generation via geospatial filtering, feature engineering (distance, popularity, personalization, context like time and weather), ranking model choice, cold‑start handling for users and places, exploration vs. exploitation strategy, spam/fraud filtering, privacy considerations, latency/throughput budgets, and an online A/B testing and monitoring plan.
Quick Answer: This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.