Design a restaurant recommendation system

ML System Design: Restaurant Recommendations (Delivery App)

You are designing a restaurant recommendation system for a food delivery marketplace (e.g., a home feed that ranks restaurants for a user).

Goal

Rank candidate restaurants for each user session to maximize long-term business and user value.

Requirements

• Use cases :
  • Home feed ranking (personalized).
  • Cold-start support for new users and new restaurants.
• Constraints / realities :
  • Users have location and delivery radius; restaurant availability changes (hours, busy/closed, delivery coverage).
  • Items/restaurants can be out of stock; ETA and fees matter.
  • Must handle bias/fairness concerns (e.g., exposure for new/small restaurants) and fraud/quality issues.
• Outputs :
  • A ranked list of restaurants with optional explanations (“Because you liked X”).

What to cover

1. Define objective(s) and offline/online success metrics .
2. Candidate generation vs. ranking (two-stage or multi-stage).
3. Feature design (user, restaurant, context, geography, time).
4. Model choices (e.g., retrieval model + ranking model) and why.
5. Training data, labels, and handling position bias.
6. Online serving architecture (latency budget, caching, feature store).
7. Exploration/exploitation, cold start, and business rules.
8. Experimentation (A/B testing), monitoring, and debugging.
9. Failure modes and how you would mitigate them.