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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References
McNee, S.M., Istvan, A., et al.: On the recommending of citations for research papers. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, pp. 116–125 (2002)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42, 177–196 (2001)
Duma, D., Liakata, M., Clare, A., Ravenscroft, J., Klein, E.: Applying core scientific concepts to context-based citation recommendation. In: Proceedings of LREC (2016)
Ebesu, T., Fang, Y.: Neural citation network for context-aware citation recommendation. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Yang, C., Liu, Z.Y., Zhao, D.L., Sun, M.S., Chang, E.Y.: Network representation learning with rich text information. In: International Joint Conference on Artificial Intelligence (2015)
Pan, S.R, Wu, J., Zhu, X.Q, Zhang, C.Q, Wang, Y.: Tri-party deep network representation. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York City, NY, USA, pp. 701–710 (2016)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. In: ICLR Workshop (2016)
Dai, Q.Y., Li, Q., Tang, J., Wang, D.: Adversarial network embedding. arXiv preprint arXiv:1711.07838 (2017)
Pan, S.R., Hu, R.Q., Long, G.D., Jiang, J., Yao, L., Zhang, C.Q.: Adversarially Regularized Graph Autoencoder. arXiv preprint arXiv:1802.04407v1 (2018)
Bethard, S., Jurafsky, D.: Who should I cite: learning literature search models from citation behavior. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2010), pp. 609–618 (2010)
Dai, T., Zhu, L., Cai, X.Y., Pan, S.R., Yuan, S.: Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network. J. Ambient Intell. Hum. Comput. 9, 957–975 (2017)
Shaparenko, B., Joachims, T.: Identifying the original contribution of a document via language modeling. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 696–697 (2009)
Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: AAAI, pp. 1145–1152 (2016)
Ou, M., Cui, P., Pei, J., et al.: Asymmetric transitivity preserving graph embedding. In: KDD, pp. 1105–1114 (2016)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, (2016)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS (2016)
Radev, D.R., Muthukrishnan, P., Qazvinian, V.: The ACL anthology network corpus. Lang. Resour. Eval. 47(4), 919–944 (2013)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feed forward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)
King, D.P., Ba, J.L. Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)
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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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