Among widely used recommendation methods, singular value decomposition (SVD) based approaches are the most successful ones. Although SVD-based methods are effective, they suffer from the problem of data sparsity, which could lead to poor recommendation quality. This paper proposes a novel imputation-based recommendation method, called the imputation-based SVD (ISVD), to solve the problem of data sparsity in SVD-based methods. Firstly, we propose a neighbor selection algorithm based on a similarity measure for users and items. In this algorithm, we set two thresholds to select effective neighbors for each user and item. Secondly, we generate the imputed data according to the neighbors’ ratings. Finally, we imputed these data into the SVD framework. By using imputed training data in SVD, our method can learn the prediction model accurately. We have tested our method on the MovieLens 100k, MovieLens 1M, Netflix and Filmtrust datasets. Experiment results show that our method outperforms the state-of-the-art ones. This study not only offers new insights into generating imputed data but also provides a guide to the alleviation of data sparsity in SVD-based methods.