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

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

Design a Restaurant Recommendation System for Food Apps

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in designing end-to-end machine learning recommendation systems, including retrieval and ranking model choices, real-time contextualization (e.g., location and time), cold-start handling for users and restaurants, feature engineering, and evaluation methodology.

Designing a Restaurant Recommendation System for a Food-Ordering App

Context

You are tasked with designing an end-to-end recommendation system that suggests restaurants to users within a food-ordering app. The system should handle personalized ranking, real-time context (e.g., location, time of day, delivery constraints), and the cold-start problem for both new users and new restaurants.

Question

Describe, end-to-end, how you would design a restaurant recommendation system.

Which machine-learning models are suitable at different stages of the system (e.g., retrieval, ranking, re-ranking)?
How would you handle cold-start restaurants and cold-start users?
How would you evaluate recommendation quality offline and online?

Hints to consider:

Data requirements and feature design
Collaborative filtering vs. content-based models
Handling implicit feedback
A/B testing metrics such as CTR and conversion

Solution

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