The extreme sparsity of the rating data seriously affects the recommendation quality of there commendation system. In order to alleviate the problem of data sparsity, some convolutional neural network (CNN)-based models make full use of text data to improve the recommendation accuracy. However,due to the inherent properties of the traditional convolutional network, it can only extract features in a fixed position, and rely on the primitive bounding box based feature extraction, thus ignoring the exibility of the traditional convolution. In this paper, we adopt a exible convolutional network called deformable convolutional network (DCN), which extends the convolution transformation model capability by addingan offset layer to the traditional convolution layer, and then propose a novel deformable convolutional network matrix factorization (DCNMF) recommendation model. Specifically, we combine the DCN with word embedding to deeply capture the contextual information of document and build a latent model, which is incorporated into the probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct extensive experiments on the real-world data sets, and the experimental results show that the DCNMF outperforms the compared benchmarks.
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