Design a product-feed recommendation system

Design an end-to-end recommendation system that generates a personalized product feed for users.

What to cover

• Requirements : user experience goals (latency, freshness), business goals (CTR/conversion/revenue), and constraints.
• Data : user events (views/clicks/purchases), product catalog attributes, embeddings, etc.
• Modeling approach : candidate generation + ranking (and possibly re-ranking).
• Serving architecture : online inference, caching, feature computation, retrieval stores.
• Training pipeline : offline training, labels, negative sampling, feature/embedding pipelines.
• Cold start : new users / new products.
• Exploration & experimentation : A/B tests, online metrics, guardrails.
• Safety/compliance (as appropriate): privacy, abuse, bias.

Assume a large-scale consumer product with millions of users and a large catalog.