The collaborative filtering recommendation algorithm is divided into user-based and item-based recommendation algorithms. Collaborative filtering recommendation algorithm had data-sparseness and scalability and cold-start problems. This paper mainly studied the collaborative filtering recommendation algorithm based on the users or Items and SVD, Slope-One, KNN. The optimization of SVD algorithm which considers the dimension of the feature space used dimension reduction to improve data-sparseness problem. Using the Hadoop distribution platform to improve the scalability problem. Experimental result shows that the similarity computation method based on Boolean data has better result and the optimization of Slope-One and SVD algorithm have better recommendation result based on MovieLens data set.