Develop a Restaurant-Recommendation Engine with Logistic Regression
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.
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Jul 12, 2025, 6:59 PM
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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.
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