How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Meta during technical interviews.

Design nearby place recommendations

Last updated: Apr 24, 2026

Quick Overview

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.

Real‑Time Nearby Places Recommendation System

Context

Design a mobile feature that recommends nearby places (e.g., restaurants, shops, attractions) to a user in real time as they move. The system should work globally, handle dense urban and sparse rural areas, respect user privacy, and scale to high QPS.

Requirements

Define objectives and success metrics.
Describe data sources: maps/POIs and user behavior.
Candidate generation using geospatial filtering.
Feature engineering: distance, popularity, personalization, context (time/weather).
Ranking model choice and training signals.
Cold‑start strategies for new users and new places.
Exploration vs. exploitation strategy.
Spam/fraud filtering.
Privacy considerations.
Latency/throughput budgets and system architecture.
Online A/B testing and monitoring plan.

Solution

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