You are building an app recommendation system for a mobile app store.
Goal
Recommend apps to a user on surfaces such as:
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Home feed / “Recommended for you”
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Category pages
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Search results (optional extension)
Requirements (assume if not specified)
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Personalized ranking
for each user.
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Cold start
support for new users and new apps.
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Real-time adaptation
to recent user actions (clicks/installs) within minutes.
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Business constraints:
respect policy/safety filters and optionally support sponsored placements.
Input signals
You may use:
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User events: impressions, clicks, installs, uninstalls, time spent, ratings
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App metadata: category, text description, tags, developer, price, locale, device compatibility
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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:
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Overall architecture (offline + online)
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Candidate generation and ranking approach
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Feature engineering and model choices
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Data/label definitions and evaluation metrics
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Cold-start, exploration, and feedback-loop mitigation
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Monitoring, A/B testing, and reliability considerations