Recommendation System Based on Generative Adversarial Network with Graph Convolutional Layers

Abstract

A graph convolutional generative adversarial network (GCGAN) is proposed to provide recommendations for new users or items. To maintain scalability, the discriminator was improved to capture the latent features of users and items, using graph convolution from a minibatch-sized bipartite graph. In the experiment using MovieLens, it was confirmed that the proposed GCGAN had better performance than the conventional CFGAN, when MovieLens 1M was employed with sufficient data. The proposed method is characterized in such a manner that it can learn domain information of both, users and items, and it does not require to relearn a model for a new node. Further, it can be developed for any service having such conditions, in the information recommendation field.