Design a Product or Video Recommendation System
GoogleMachine Learning Engineer · Technical Screen
Last updated
This question evaluates competency in designing large-scale recommendation systems, encompassing machine learning modeling, candidate generation and ranking, data engineering, online serving, feedback-driven learning, evaluation, and experimentation.
Design a recommendation system for a large consumer platform. The platform may recommend either products in an e-commerce feed or videos in a media feed.
Your design should cover:
- The main user-facing recommendation surfaces.
- Online and offline data sources.
- Candidate generation.
- Ranking and re-ranking.
- Feedback signals such as clicks, views, purchases, watch time, likes, skips, hides, and negative feedback.
- Model training and evaluation.
- Cold-start handling for new users and new items.
- Online serving architecture and latency constraints.
- Experimentation, monitoring, and guardrails.
Solution walkthrough is for members
Get the full walkthrough with reference code and complexity analysis, plus Kitty Coach at 300 messages a day. Hints, the code runner and community answers stay free.