Building a restaurant‑recommendation feature with Nearby Friends signals

Q: Building a restaurant‑recommendation feature with Nearby Friends signals

This question evaluates a data scientist's competencies in real-time location-based recommendation systems, experimentation and metrics design, data integration, and considerations around privacy, fairness, and operational trade-offs.

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Question

Real-time Nearby Eateries Recommendation (Meta)

Context

Meta wants to leverage real‑time, opt‑in location from Nearby Friends to recommend nearby eateries users might like, balancing utility with privacy, battery, and fairness. You are asked to outline the product and data approach, forecast behavioral impact, define validation and success metrics, and anticipate risks.

Questions

How would you use Nearby Friends location to build a new feature? (Hint: live coordinates, activity patterns, social graph)
Why create restaurant recommendations and how might behavior change? (Hint: offline conversion, social sharing)
What data sets are required? (Hint: merchant POI, user preference profile, time‑of‑day context)
How would you validate model effectiveness? (Hint: A/B test, click‑to‑visit rate)
Which key metrics post‑launch? (Hint: recommendation uptake, purchase conversion)
What negative impacts could arise? (Hint: privacy concerns, merchant bias, battery drain)
How does restaurant recommendation differ from ‘People You May Know’? (Hint: real‑time context, content diversity)

Building a restaurant‑recommendation feature with Nearby Friends signals

Real-time Nearby Eateries Recommendation (Meta)

Context

Questions

Solution

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