Abstract:  Due to the fact that the data sparseness leads to the low accuracy of a collaborative filtering recommendation system, this paper proposed a collaborative filtering algorithm based on Radial Basis Function (RBF) interpolation and SVM. The algorithm first uses the RBF interpolation to fill the missing data in training data set, and then a SVM classifier is introduced to predict the label for testing data by using the interpolated data set as training set. The test result shows that the method overcomes the impact of data quality on the recommended algorithm, and it outperforms other methods in accuracy and stability.
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Key words: collaborative filtering, radial basis function, interpolation, support vector machine