Design a cold-start video recommender

Q: Design a cold-start video recommender

This question evaluates proficiency in ML system design for recommender systems, focusing on cold-start handling, integration of content and graph features, candidate generation and ranking, exploration–exploitation trade-offs, real-time feedback and model updates, and experimental metrics and guardrails.

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Design a cold-start recommendation pipeline for a short-video platform. The system must work for both new users with little or no interaction history and new videos with little or no engagement data. You may assume graph information is available, such as user-user, user-video, creator-video, hashtag-video, and video-video relations.

Describe:

The offline and online architecture
How you would use content features and graph features
Candidate generation and ranking stages
How to handle exploration versus exploitation
Real-time feedback and model updates
Metrics, guardrails, and experiments
How the system transitions from cold-start recommendations to fully personalized recommendations

Design a cold-start video recommender

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