Employ Collaborative Filtering for Personalized Recommendation Lists

Q: Employ Collaborative Filtering for Personalized Recommendation Lists

This question evaluates competency in recommender systems and production machine learning engineering, covering collaborative filtering and embedding-based candidate generation, learning-to-rank, contextual personalization, constraint enforcement, and scalable serving architectures.

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Question

Scenario

You are releasing a new recommendation feature that must generate and assign personalized, ranked product lists for each user at scale. Users may have different roles (e.g., buyer vs. seller), and recommendations must respect contextual signals (e.g., session/device/time) as well as business or policy constraints (e.g., eligibility, age/geography restrictions).

Question

Which machine learning technique(s) would you use to create ranked recommendation lists for users? Outline a practical end-to-end approach that can scale.
How would you incorporate user role, context, and other constraints when assigning recommended items?

Expectations

Discuss and justify choices across the typical recommendation stack, including:

Candidate generation (e.g., collaborative filtering, two-tower embeddings, graph/sequence methods).
Learning-to-rank for scoring and ordering.
Contextual and/or bandit approaches for exploration and personalization.
Serving architecture considerations and how to enforce constraints during retrieval and re-ranking.

Employ Collaborative Filtering for Personalized Recommendation Lists

Scenario

Question

Expectations

Solution

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