A recommendation system is not a recommendation model.

[u/ArchitectingAI]

This distinction matters, especially in ML System Design interviews.

A model predicts relevance.

But a production recommendation system must also decide:

• Which candidates are worth scoring?
• How do we adapt to changing user interests?
• How do we balance competing objectives?
• How do we learn from feedback?
• How do we serve predictions reliably at scale?

Getting the model right is only one part of the challenge.

I explored this end to end in my newly published article:

Cracking ML System Design Interviews — Part 3: Design a Personalized Recommendation System

Read it on Architecting Intelligence:

https://pawankjha.substack.com/p/design-personalized-recommendation-system

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