Design feedback-driven recommender

Q: Design feedback-driven recommender

This question evaluates a candidate's competency in online machine learning and recommender system design, covering contextual bandits, feature engineering for users/items/context, real-time feedback processing, exploration–exploitation strategies, reward definition, offline and online evaluation, and scalable, robust architecture.

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Question

Design: Contextual Bandit Recommendation with Online Learning

You are designing an online learning recommendation system. At each user interaction:

You receive exactly 4 candidate items from an upstream candidate generator.
You must choose exactly 1 item to show the user.
You receive immediate feedback (e.g., click or dwell time).
The model must update online so that future selections improve over time.

Provide a design that covers:

Model choice (with justification) for a contextual bandit setup.
Feature engineering for users, items, and context, including handling cold start.
Feedback handling and reward definition, including delayed/implicit signals and logging for learning.
Exploration–exploitation strategy and the selection algorithm.
Offline evaluation methodology and online experimentation/monitoring.

State any minimal assumptions you need (e.g., feedback semantics, latency constraints), and make your design robust to non-stationarity and scale.

Design feedback-driven recommender

Design: Contextual Bandit Recommendation with Online Learning

Solution

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Design feedback-driven recommender

Overview

Design: Contextual Bandit Recommendation with Online Learning

Solution

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