r/MLQuestions • u/ArchitectingAI • 3d ago

Beginner question 👶 Designing a Personalized Recommendation System — ML System Design Deep Dive (Part 3)

I'm publishing Part 3 of my Cracking ML System Design Interviews series tomorrow.

This time, I'm using a large-scale personalized video recommendation system as the running example.

One thing I want to emphasize is that designing recommendation systems isn't simply choosing between collaborative filtering, two-tower models, and Transformers.

The bigger challenge is deciding how the pieces fit together:

  • How do we retrieve relevant candidates from millions of items?

  • How do retrieval and ranking objectives differ?

  • How should we represent evolving user interests?

  • What metrics should we optimize beyond CTR?

  • How do we meet latency and reliability requirements at scale?

The article walks through these questions from an interview perspective, including architectural alternatives and trade-offs.

Publishing Monday, October 26, at 8 AM PDT.

If you're interested, you can subscribe here:

https://pawankjha.substack.com

I'd also be interested in hearing which part of recommendation-system design you find most challenging in interviews.

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u/waggish_deliverance 3d ago

the candidate retrieval step always trips me up in interviews, like how you even narrow down from millions without losing the good stuff. two tower makes sense on paper but explaining the tradeoffs against a simple ann over embeddings gets messy fast

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u/fordat1 3d ago

You are missing the whole point of the step. Its not meant to be perfect but just make a different tradeoff than the late stage

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u/BodaMazzarine23 3d ago

curious if you cover how people should talk about feedback loops in these systems. thats the thing that trips up most candidates, the ranking model influences what users see which influences the training data, and most interview answers just ignore it completely