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This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Meta.

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

Develop a Restaurant-Recommendation Engine with Logistic Regression

Last updated: Mar 29, 2026

Quick Overview

This question evaluates skills in feature engineering, model selection, metric definition, and model evaluation for a recommendation problem using logistic regression, with emphasis on behavioral, demographic, social and item features.

Restaurant Recommendation Engine: Metrics, Features, Model, and Evaluation

Scenario

You are designing a restaurant recommendation engine for a social app.

Task

Define the primary business goal for restaurant recommendations and list key engagement/product metrics you would track.
Specify which features you would include in the model and why. Cover behavioral, demographic, and social features (you may add context and item features if useful).
Choose a modeling approach. Explain why logistic regression may be appropriate.
Describe how you would evaluate whether the logistic-regression model is accurate.
Define precision, recall, and accuracy; provide their formulas; state which metric(s) you would prioritize for this use case and why.

Hint: Define the target clearly, ensure features are aligned with the label (no leakage), and link the evaluation metric to the business goal.

Solution

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