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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 System to Recommend Local Restaurant Profiles

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's ability to design an end-to-end local recommender system, including competencies in data sourcing and labeling, feature engineering, candidate generation and ranking, real-time serving, latency and freshness trade-offs, and privacy-safe handling of location data.

Recommending Local Restaurant Pages in the News Feed

Context

Design a non-ads recommendation system within a large social media app to surface local restaurant business profiles in each user’s news feed. The goal is to maximize relevant engagement (e.g., profile clicks, saves, follows) while meeting latency, privacy, and user experience constraints.

Task

Describe an end-to-end design covering:

Data sources and labels
Key features (user, restaurant, and context)
Model choices for candidate generation and ranking
Real-time serving and ranking approach (including latency and freshness)
Evaluation and iteration plan (offline and online)

Assume you can use standard logging, a feature store, and nearline streams. Prioritize local relevance and privacy-safe use of location data.

Solution

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