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This is a medium 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 and Validate Initial Restaurant Recommendation Model | Meta Interview Question

Design and Validate Initial Restaurant Recommendation Model

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in designing and validating recommendation systems, including feature engineering, cold-start handling, contextual and social-graph signals, offline versus online evaluation, and reasoning about multi-objective metric trade-offs.

Restaurant Recommendations on Facebook — First-Iteration Model

Scenario

You are tasked with designing a first-iteration machine-learning system to recommend restaurants to users on Facebook across surfaces like "Nearby," Feed units, and Search.

Question

How would you design an initial recommendation model for restaurants (including features and handling cold-start)?
How would you validate whether the model is working (offline vs. online)?
If one performance metric rises while another drops, how would you interpret the trade-off and decide what to do?

Hints

Cold-start (new users/items)
Feature design (user, item, context, social/graph)
Offline vs. online validation
Multi-objective optimization

Solution

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