This is the kind of question where you either know the exploration-exploitation tradeoff cold or you fumble around explaining epsilon-greedy while the interviewer watches.
Start by clarifying the problem scope and assumptions (e.g., stationary vs non-stationary rewards, known vs unknown reward distributions). Then implement a clean, modular solution for a chosen algorithm like UCB1 or Thompson Sampling, and discuss trade-offs and potential extensions for real-world A/B testing at Wayfair.
Pro tip: Mention that in production, you'd log all pulls and rewards for offline evaluation and use a small exploration rate to handle non-stationarity. Also, highlight that bandits can reduce regret compared to traditional A/B tests, but require careful handling of delayed feedback and seasonality.
Ask about the number of arms, reward distribution, whether rewards are binary or continuous, and if the environment is stationary. Confirm if they want a specific algorithm or a general framework.
Select an algorithm like UCB1, Thompson Sampling, or epsilon-greedy based on the assumptions. Briefly explain why it's suitable (e.g., UCB1 for deterministic exploration, Thompson for Bayesian efficiency).
Write clean, modular code with a class for the bandit, methods for selecting an arm and updating rewards, and clear variable names. Include comments and handle edge cases (e.g., cold start).
Simulate with synthetic data to show the algorithm learns and converges. Discuss metrics like cumulative regret or average reward, and compare against a random baseline.
Talk about computational complexity, scalability, and how to adapt for non-stationary rewards or contextual information. Mention integration with A/B testing frameworks and potential pitfalls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.