Abstract
Currently, collaborative filtering (CF) is one of the most popular and successful recommendation technologies. However, the algorithm is easily affected by data sparsity, leading to poor recommendation accuracy. Recently, Deep Belief Nets (DBNs) have been successfully applied in many research areas including image classification and phone recognition. In this paper, to solve the data sparsity problem in CF, we propose a hybrid recommendation model based on DBNs and K-nearest neighbor (KNN) algorithm in which the user-based KNN algorithm makes predictions using the user features extracted by the DBNs. We also present efficient learning and inference methods for this hybrid model and demonstrate that DBNs can be successfully applied to CF. Finally, we carried out several experiments on MovieLens dataset which demonstrate that our hybrid model can achieve better recommendation results than some other CF methods which are widely used.
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