Contextual Bandits : Data Science Concepts

3-Second Blueprint (English)

1. Standard multi-armed bandits optimize average population reward, converging on one global action that ignores individual differences.
2. Contextual bandits condition decisions on an instance feature vector, using predictive models to estimate reward for each candidate arm.
3. Combining contextual epsilon-greedy exploration with per-user covariates boosts personalization at the cost of slower model convergence.