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Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. Most research up to this point has focused on improving the accuracy of recommender systems. However, considering the range of userpsilas interests covered, recommendation diversity is also important. In this paper we propose a novel topic diversity metric which explores hierarchical domain knowledge, and evaluate the recommendation diversity of the two most classic collaborative filtering (CF) algorithm with movielens dataset.