Design a System to Recommend Local Restaurant Profiles

Q: Design a System to Recommend Local Restaurant Profiles

This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design a System to Recommend Local Restaurant Profiles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Q: 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.

Q: What difficulty level is this interview question?

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

Q: What role is this question designed for?

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

Question

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.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify the task, data shape, labels, constraints, and evaluation metric.
State assumptions behind the math or modeling technique you choose.
Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

Correct definitions and formulas where the prompt requires them.
A practical explanation of how the method behaves on real data.
Trade-offs, failure modes, diagnostics, and mitigation strategies.
Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

How would noisy labels, class imbalance, or distribution shift affect the answer?
What would you monitor after deployment?
Which baseline would you compare against first?

Design a System to Recommend Local Restaurant Profiles

Quick Overview

Design a System to Recommend Local Restaurant Profiles

Recommending Local Restaurant Pages in the News Feed

Context

Task

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer