Design a Restaurant Recommendation System for Food Apps

Q: Design a Restaurant Recommendation System for Food Apps

This question evaluates a candidate's competency in designing end-to-end machine learning recommendation systems, including retrieval and ranking model choices, real-time contextualization (e.g., location and time), cold-start handling for users and restaurants, feature engineering, and evaluation methodology.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Designing a Restaurant Recommendation System for a Food-Ordering App

Context

You are tasked with designing an end-to-end recommendation system that suggests restaurants to users within a food-ordering app. The system should handle personalized ranking, real-time context (e.g., location, time of day, delivery constraints), and the cold-start problem for both new users and new restaurants.

Question

Describe, end-to-end, how you would design a restaurant recommendation system.

Which machine-learning models are suitable at different stages of the system (e.g., retrieval, ranking, re-ranking)?
How would you handle cold-start restaurants and cold-start users?
How would you evaluate recommendation quality offline and online?

Hints to consider:

Data requirements and feature design
Collaborative filtering vs. content-based models
Handling implicit feedback
A/B testing metrics such as CTR and conversion

Design a Restaurant Recommendation System for Food Apps

Designing a Restaurant Recommendation System for a Food-Ordering App

Context

Question

Solution

Comments (0)

Design a Restaurant Recommendation System for Food Apps

Overview

Designing a Restaurant Recommendation System for a Food-Ordering App

Context

Question

Solution

Comments (0)