A Novel Personalized Citation Recommendation Approach Based on GAN

With the explosive growth of scientific publications, researchers find it hard to search appropriate research papers. Citation recommendation can overcome this obstacle. In this paper, we propose a novel approach for citation recommendation by applying the generative adversarial networks. The generative adversarial model plays an adversarial game with two linked models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability which a sample came from the training data rather than G. The model first encodes the graph structure and the content information to obtain the content-based graph representation. Then, we encode the network structure and co-authorship to gain author-based graph representation. Finally, the concatenation of the two representations will be acted as the node feature vector, which is a more accurate network representation that integrates the author and content information. Based on the obtained node vectors, we propose a novel personalized citation recommendation approach called CGAN and its variation VCGAN. When evaluated on AAN dataset, we found that our proposed approaches outperform existing state-of-the-art approaches.