##### Scenario Building a restaurant recommendation feature for a food-ordering app. ##### Question Describe end-to-end how you would design a restaurant recommendation system. Which machine-learning models are suitable, and how would you handle cold-start restaurants or users? How would you evaluate the quality of your recommendations both offline and online? ##### Hints Cover data requirements, collaborative filtering vs. content-based models, implicit feedback, A/B testing metrics like CTR and conversion.

Meta machine learning system-design prompt on restaurant recommendations for food apps, covering retrieval and ranking models, features, cold start, implicit feedback, offline metrics, A/B testing, and marketplace guardrails.

How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Design a Restaurant Recommendation System for Food Apps

Design a Restaurant Recommendation System for a Food-Ordering App

You are designing an end-to-end recommendation system that suggests restaurants to users in a food-ordering app. The system must support personalized ranking, real-time context, delivery constraints, and cold-start handling.

Constraints & Assumptions

The app has user interaction logs, restaurant metadata, menu data, location data, and delivery outcome data.
Recommendations should include only eligible restaurants, such as open restaurants within delivery range and capacity constraints.
The system should optimize business and user value while protecting delivery quality and marketplace fairness.
Explain both offline model quality and online product impact.

Clarifying Questions to Ask

What is the primary goal: order conversion, retention, revenue, exploration, or delivery reliability?
Which surface is being ranked: homepage, search, cuisine page, reorder module, or push notification?
What feedback signals are logged: impressions, clicks, add-to-cart, orders, ratings, cancellations, refunds?
Are there fairness or exposure constraints for new restaurants, small restaurants, or cuisine diversity?

What a Strong Answer Covers

Problem framing as candidate retrieval, ranking, and re-ranking under eligibility constraints.
Data and features: user history, restaurant metadata, menu text, cuisine, price, ratings, location, distance, delivery time, fees, promotions, time of day, and inventory/capacity.
Model choices such as collaborative filtering, matrix factorization, two-tower retrieval, gradient-boosted ranking, neural ranking, content-based methods, and contextual bandits for exploration.
Handling implicit feedback, exposure bias, position bias, and delayed labels.
Cold-start strategies for new users and restaurants using content features, popularity priors, onboarding preferences, exploration buckets, and uncertainty-aware ranking.
Offline evaluation with recall@K, NDCG, MAP, calibration, coverage, diversity, and counterfactual/off-policy caveats.
Online A/B metrics such as CTR, add-to-cart, orders, conversion, revenue, retention, delivery quality, cancellation, refunds, latency, and fairness guardrails.

Follow-up Questions

How would you prevent the system from only recommending already popular restaurants?
How would you handle restaurants that are temporarily overloaded?
What would you do if offline NDCG improves but online conversion falls?
How would you introduce exploration safely?

Design a Restaurant Recommendation System for a Food-Ordering App

Constraints & Assumptions

The app has user interaction logs, restaurant metadata, menu data, location data, and delivery outcome data.

Recommendations should include only eligible restaurants, such as open restaurants within delivery range and capacity constraints.

The system should optimize business and user value while protecting delivery quality and marketplace fairness.

Explain both offline model quality and online product impact.

Clarifying Questions to Ask

What is the primary goal: order conversion, retention, revenue, exploration, or delivery reliability?

Which surface is being ranked: homepage, search, cuisine page, reorder module, or push notification?

What feedback signals are logged: impressions, clicks, add-to-cart, orders, ratings, cancellations, refunds?

Are there fairness or exposure constraints for new restaurants, small restaurants, or cuisine diversity?

What a Strong Answer Covers

Problem framing as candidate retrieval, ranking, and re-ranking under eligibility constraints.

Data and features: user history, restaurant metadata, menu text, cuisine, price, ratings, location, distance, delivery time, fees, promotions, time of day, and inventory/capacity.

Model choices such as collaborative filtering, matrix factorization, two-tower retrieval, gradient-boosted ranking, neural ranking, content-based methods, and contextual bandits for exploration.

Handling implicit feedback, exposure bias, position bias, and delayed labels.

Cold-start strategies for new users and restaurants using content features, popularity priors, onboarding preferences, exploration buckets, and uncertainty-aware ranking.

Offline evaluation with recall@K, NDCG, MAP, calibration, coverage, diversity, and counterfactual/off-policy caveats.

Online A/B metrics such as CTR, add-to-cart, orders, conversion, revenue, retention, delivery quality, cancellation, refunds, latency, and fairness guardrails.

Follow-up Questions

How would you prevent the system from only recommending already popular restaurants?

How would you handle restaurants that are temporarily overloaded?

What would you do if offline NDCG improves but online conversion falls?

How would you introduce exploration safely?

Design a Restaurant Recommendation System for Food Apps

Quick Overview

Design a Restaurant Recommendation System for Food Apps

Design a Restaurant Recommendation System for a Food-Ordering App

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer

Design a Restaurant Recommendation System for Food Apps

Quick Overview

Design a Restaurant Recommendation System for Food Apps

Design a Restaurant Recommendation System for a Food-Ordering App

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Write your answer