Design a recommendation system from scratch

Q: Design a recommendation system from scratch

This question evaluates expertise in recommender systems and related competencies including machine learning-based candidate generation and ranking, data instrumentation and logging, cold-start strategies, and large-scale system design concerns such as latency, throughput, and reliability.

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Recommendation System Design (two scenarios)

Design a recommendation system from scratch. Cover both scenarios:

Location/POI recommendation: Recommend places (restaurants, attractions, stores) to a user.
- Users may be searching, browsing a map, or opening a “For You” page.
- Relevance depends on location, time, preferences, popularity, and context.
Short-video recommendation: Recommend a ranked feed of short videos.
- Users scroll continuously; feedback is implicit (watch time, skips) and explicit (likes, follows).

What to cover

Product goals and success metrics
Data collection: what events/data to log and how to obtain bootstrap data
High-level architecture (offline training + online serving)
Candidate generation and ranking strategy
Handling cold start (new users/items)
Latency/scale considerations and reliability
Experimentation (A/B tests) and iteration loop

Design a recommendation system from scratch

Quick Overview

Recommendation System Design (two scenarios)

What to cover

Solution

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