Develop a Restaurant-Recommendation Engine with Logistic Regression

Q: Develop a Restaurant-Recommendation Engine with Logistic Regression

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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Question

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.

Develop a Restaurant-Recommendation Engine with Logistic Regression

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

Scenario

Task

Solution

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