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The booming growth of cloud manufacturing services provides users with more choices. However, cloud manufacturing service recommendation remains a challenging issue due to numerous similar candidate services and diverse user preferences. The purpose of this paper is to provide an efficient and accurate cloud manufacturing service recommendation method. A spectral clustering algorithm is first designed to cluster the cloud manufacturing services. Then the candidate rating service set is constructed based on the service clusters by service function comparison and parameter matching. Finally, an improved Slope one algorithm, which integrates user similarity and service similarity, is proposed to rate the cloud manufacturing services. The top-k services with the highest scores are recommended to the users. Experiments show that the proposed method can provide more accurate service rating with less time consumption. The service recommendation performance of this method is also proved to be superior to other methods in terms of precision, recall, and F-score.