Abstract: In the recommendation system, data sparsity is one of the important factors that seriously affect the accuracy of recommendation results. Aiming at the data sparsity, this paper proposes a hybrid neural network collaborative filtering score prediction model convolutional-denosing auto-encoder (CDAE) to perform prediction scoring for solving the problem of data sparsity. The CDAE model combines a convolutional neural network (CNN) and a denoising auto-encoder (DAE) neural network. Firstly, the vectorized user review data is trained by the CNN to obtain a user feature vector matrix. Secondly, the user feature vector matrix is used as the initial weight of the DAE neural network, and the user-item prediction score is obtained by training the auto-encoder neural network in combination with user rating data. Accordingly, user-based collaborative filtering recommendations can be made. In the paper, the proposed convolutional-denosing auto-encoder collaborative filtering (CDAECF) model is experimentally verified by comparing with the experimental data set of movielens-1M. Experiment results prove that the CDAECF model can effectively combine implicit and explicit feedback data, and performs a higher recommendation accuracy rate.

Key words: convolutional neural network, denoising auto-encode neural network, collaborative filtering, sparsity