Design an app-store app recommendation system

Q: Design an app-store app recommendation system

This is a ML System Design interview question from Google for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

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You are building an app recommendation system for a mobile app store.

Goal

Recommend apps to a user on surfaces such as:

Home feed / “Recommended for you”
Category pages
Search results (optional extension)

Requirements (assume if not specified)

Personalized ranking for each user.
Cold start support for new users and new apps.
Real-time adaptation to recent user actions (clicks/installs) within minutes.
Business constraints: respect policy/safety filters and optionally support sponsored placements.

Input signals

You may use:

User events: impressions, clicks, installs, uninstalls, time spent, ratings
App metadata: category, text description, tags, developer, price, locale, device compatibility
Context: country, language, device type, time

Output

For a given user and context, produce a ranked list of apps (top-K) with latency suitable for an online product.

Describe:

Overall architecture (offline + online)
Candidate generation and ranking approach
Feature engineering and model choices
Data/label definitions and evaluation metrics
Cold-start, exploration, and feedback-loop mitigation
Monitoring, A/B testing, and reliability considerations

Design an app-store app recommendation system

Goal

Requirements (assume if not specified)

Input signals

Output

Solution

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