Determine Facebook's Restaurant Recommendation Viability Using Data
Quick Overview
Evaluates product sense and analytics for deciding whether Facebook should launch restaurant recommendations. Strong answers size demand and supply, estimate incremental user and business value, identify required data, and propose experiments with privacy, quality, and cannibalization guardrails.
Determine Facebook's Restaurant Recommendation Viability Using Data
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
Facebook may launch a restaurant-recommendation product.
##### Question
How would you decide whether adding restaurant recommendations is worthwhile for Facebook, and which data would you use?
##### Hints
Size the addressable market, incremental engagement, revenue potential, cannibalization.
Quick Answer: Evaluates product sense and analytics for deciding whether Facebook should launch restaurant recommendations. Strong answers size demand and supply, estimate incremental user and business value, identify required data, and propose experiments with privacy, quality, and cannibalization guardrails.
Determine Facebook's Restaurant Recommendation Viability Using Data
Facebook may launch a restaurant-recommendation product that helps people discover places to eat using signals such as location, social activity, local business Pages, and user interactions.
Constraints & Assumptions
Focus on whether the product is worth building, not on the final ranking algorithm.
Assume access to Facebook product logs, local business Page data, coarse location signals where permitted, social graph signals, and experiment infrastructure.
Do not assume restaurant recommendations are valuable just because users like restaurants elsewhere; estimate incremental value for Facebook.
Include user value, business value, opportunity cost, and guardrails.
Clarifying Questions to Ask Guidance
Which surface is being considered: Search, Marketplace, Feed, local discovery, or a dedicated tab?
What is the primary objective: engagement, local business revenue, retention, or strategic expansion?
Is the product meant to recommend restaurants generally or only when a user expresses local intent?
Are privacy, location, or market constraints limiting which data can be used?
Part 1 - Size the Opportunity
How would you decide whether restaurant recommendations are worthwhile for Facebook?
What This Part Should Cover Guidance
Demand signals from local searches, restaurant Page views, check-ins, saves, reviews, event/location behavior, and external-intent proxies if available.
Supply readiness: business Page coverage, data quality, freshness, hours, categories, photos, and geographic coverage.
Incremental value: new sessions, downstream actions, retention, local business value, and monetization potential.
Opportunity cost and cannibalization of existing Facebook surfaces.
Part 2 - Identify Data and Analyses
Which data would you use, and what analyses or experiments would you run?
What This Part Should Cover Guidance
User, business, social, context, and interaction data needed to estimate demand and relevance.
Cohort, market, and use-case segmentation, such as travelers, local explorers, food-interested users, and active Page followers.
A pilot or A/B test that measures incremental restaurant actions, quality feedback, and long-term engagement.
Guardrails for irrelevant recommendations, privacy concerns, spammy businesses, and degraded core engagement.
What a Strong Answer Covers Guidance
A strong answer frames the decision as a product investment case: size demand, assess supply, estimate incremental user and business value, test causally, and define clear launch or no-launch criteria.
Follow-up Questions Guidance
How would you handle cold-start restaurants with little Facebook activity?
If restaurant recommendations increase clicks but not repeat usage, what would you conclude?
Which markets would you choose for an initial pilot and why?