We study online recommendation of item sets that are high in both rating and diversity. We formalize our problem as a contextual bandit problem in which the reward of a recommended set is the rating sum of the member items plus the diversity of the set. In our problem setting, each item is represented as a feature vector (context) in a multi-dimensional metric space. Its rating is assumed to be the noisy value of some linear function of its feature vector, and the diversity of an item set is measured using the Sum diversity for the feature metric space. We develop an for this contextual bandit problem that guarantees a \(O(\sqrt{n}{\ln n})=\tilde{O}(\sqrt{n})\) upper bound on (1/2)-regret. According to our experimental results on a benchmark dataset, the proposed algorithm outperforms existing algorithms in terms of not only our combined reward but also the reward of rating sum only.