I went with a contextual bandit setup using a linear model with upper confidence bounds, which felt safe but I second-guessed myself mid-answer and started rambling about neural bandits before snapping back.
Start by framing the problem as a contextual bandit with a slate of four candidates, then propose a model like a neural network that scores each candidate and selects the highest-scoring one with an epsilon-greedy or Thompson sampling exploration strategy. Walk through feature engineering, feedback incorporation via online learning, and evaluation using both offline metrics (e.g., replay) and online A/B tests.
Pro tip: Emphasize the importance of logging propensities and using counterfactual evaluation to avoid feedback loops, and mention that you'd start with a simple model and iterate, showing pragmatism and awareness of production constraints.
Ask about latency, scale, cold-start, and business metrics to scope the system. Confirm that feedback is immediate and that the model must update continuously.
Propose a contextual bandit approach with a neural network or gradient-boosted trees to score candidates. Engineer user, item, and context features, including embeddings and interaction history.
Use epsilon-greedy or Thompson sampling to balance exploration and exploitation. Log propensities and update the model online using incremental learning or periodic retraining.
Offline: use replay or counterfactual estimators to evaluate new policies. Online: run A/B tests with guardrail metrics and monitor for feedback loops and novelty effects.
Set up monitoring for model drift, latency, and business metrics. Plan for continuous improvement and handling of edge cases like cold-start and position bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.