DiCGAN: A Dilated Convolutional Generative Adversarial Network for Recommender Systems

Generative Adversarial Network (GAN) has recently been introduced into the domain of recommendation due to its ability of learning the distribution of users’ preferences. However, most existing GAN-based recommendation methods only exploit the user-item interactions, while ignoring to leverage the information between user’s interacted items. On the other hand, Convolutional Neural Network (CNN) has shown its power in learning high-order correlations. In this paper, combining with the strengths of both GAN and CNN, we propose a Dilated Convolutional Generative Adversarial Network (DiCGAN) for recommendation, in which we first embed the interacted items of per user into an image in a latent space, and then use several dilated convolutional filters and a vertical convolutional filter to capture the high-order correlations among the interacted items. Moreover, an attention module is employed before convolution to generate attention maps for adaptive feature refinement. Experiments on several public datasets verify the superiority of DiCGAN over several baselines in terms of top-N recommendation. Further more, our experimental results show that when the dataset is more large and sparse, the performance gain of DiCGAN is also more significant, demonstrating the effectiveness of the CNN component in extracting high-order correlations from interacted data for better performance.