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Collaborative Filtering Data Sparsity Base on Radial Basis Function

Xingli Liu


Faced with the problem of information overload, for the user can find the information you need from the vast of data as soon as possible, namely using collaborative filtering algorithm to solve the data sparse problem. In collaborative filtering using radial basis function neural network to solve the sparsity problem in collaborative filtering. The radial basis function neural network was constructed using a new method. The missing data in users? evaluation matrix was predicted by the radial basis function neural network, and the accuracy of the user similarity calculations was improved. The experimental results demonstrate that the proposed method can well-targeted recommend items for users compared with the classical collaborative filtering algorithm, and it can effectively alleviate the sparsity problem in collaborative filtering.


Radial basis function, collaborative filtering, mean absolute error, neural network.

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