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Design "Restaurants You May Know" Recommendation Algorithm

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in recommender systems, product analytics, A/B experimentation, causal inference, feature engineering, and handling business constraints such as cold-start and attribution.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Design "Restaurants You May Know" Recommendation Algorithm

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Food-delivery app wants to launch a "Restaurants You May Know" recommendation feature on the home page. ##### Question Why might users and the business need this feature? Describe how you would design the recommendation algorithm and end-to-end user experience. What experiment or metrics would you use to evaluate the feature’s impact? ##### Hints Think CTR, order conversion, retention; propose A/B test and attribution window.

Quick Answer: This question evaluates a data scientist's competency in recommender systems, product analytics, A/B experimentation, causal inference, feature engineering, and handling business constraints such as cold-start and attribution.

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Meta
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
34
0

Food-Delivery: "Restaurants You May Know" Recommendations

Context

You are working on a food-delivery app with a personalized home page. The team wants to add a new module called "Restaurants You May Know" to help users discover relevant restaurants they haven’t ordered from recently.

Assume standard clickstream and order events are available (impressions, clicks, add-to-cart, orders, order value), and the module links to the restaurant’s menu page.

Questions

  1. Why might users and the business need this feature?
  2. How would you design the recommendation algorithm (from candidate generation to ranking), including handling cold start and business constraints?
  3. How would you design the end-to-end user experience (placement, explainability, controls, feedback)?
  4. What experiment design and metrics would you use to evaluate the feature’s impact (include CTR, order conversion, retention, and an attribution window)?

Solution

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