Abstract
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.
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Zhang, Y., Yang, L., Cai, X., Dai, H. (2018). A Novel Personalized Citation Recommendation Approach Based on GAN. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_26
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