Design a bandit recommender that learns from feedback

At each interaction the system must show one item out of a few candidates and then sees whether the user responded. Design a recommender that learns from that feedback online: the exploration strategy (epsilon-greedy, UCB, Thompson sampling, contextual bandits), the features, how you measure regret and success, and how it runs in production.