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Recommender system is one of the most important technologies in electronic commerce. And the collaborative filtering is almost the popular approach used in the recommender systems. With the development of electronic commerce systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Sparsity of users' ratings is the major reason causing the poor quality. To address this issue, a collaborative filtering recommendation algorithm based on singular value decomposition (SVD) smoothing is presented. This approach predicts item ratings that users have not rated by the employ of SVD technology, and then uses Pearson correlation similarity measurement to find the target users' neighbors, lastly produces the recommendations. The collaborative filtering recommendation algorithm based on SVD smoothing can alleviate the sparsity problems of the user item rating dataset, and can provide better recommendation than traditional collaborative filtering algorithms.