Contextual Bandit

A class of algorithms in reinforcement learning that extends the multi-armed bandit framework by incorporating contextual features about users or environments. The algorithm dynamically selects the optimal content variant based on user attributes and is commonly applied to personalized recommendation and online advertising optimization.