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The sparsity of user-item rating matrix will reduce the performance of collaborative filtering algorithm in news recommendation system. In order to overcome the problem, we predict the values of user-item rating matrix combining two approaches: co-clustering and Radial Basis Function network (RBF). Co-clustering algorithm simultaneous cluster the rows and columns of the user-item rating matrix. It can cluster the matrix into several small matrix with high similarity. We take advantage of the similarity of a cluster and then predict the values using RBF network. This method can complement the sparse user-item rating matrix and improve the accuracy of news recommendation system by collaborative filtering algorithm. Experimental results on Xiamen University campus news data set demonstrate the efficiency and effectiveness of the proposed missing value prediction method.

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Latent factor model (LFM) is a classical model-based collaborativefiltering approach that explains the user-item association by characterizing both items and users on latent factors inferred from rating patterns. Due to high data volume, we consider whether it is reasonable that LFM is a linear model with interaction between users and items. Therefore, we propose a hierarchical model, which groups items or users by item features or user features, and establish a LFM on each class. To avoid overfitting and comparison, we use different kinds of regularization to punish the model due to different meanings of regularization. Then we apply the hierarchical model to news recommendation. We use latent dirichlet allocation (LDA) to analyse news, which gets the topic distribution of news that can be used as news feature. The experimental results show the superiority of the hierarchical model in the news recommendation system with higher accuracy than other algorithms.