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.

1. [1] T. Sugimoto et al., “A Recommendation System with the Use of Comprehensive Trend Indication Based on Weighted Complete Graph,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.2, pp. 266-272, doi: 10.20965/jaciii.2012.p0266, 2012.
2. [2] Y. Takama et al., “Personal Values-Based Item Modeling and its Application to Recommendation with Explanation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.6, pp. 867-874, doi: 10.20965/jaciii.2016.p0867, 2016.
3. [3] J. Davidson et al., “The YouTube video recommendation system,” Proc. of the 4th ACM Conf. on Recommender systems, pp. 293-296, 2010.
4. [4] A. Sharma et al., “Estimating the causal impact of recommendation systems from observational data,” Proc. of the 16th ACM Conf. on Economics and Computation, pp. 453-470, 2015.
5. [5] C. A. Gomez-Uribe and N. Hunt, “The netflix recommender system: Algorithms, business value, and innovation,” ACM Trans. on Management Information Systems (TMIS), Vol.6, No.4, Article No.13, 2016.
6. [6] I. Goodfellow et al., “Generative adversarial nets,” Advances in Neural Information Processing Systems (NIPS 2014), Vol.27, pp. 2672-2680, 2014.
7. [7] J. Wang et al., “IRGAN: A minimax game for unifying generative and discriminative information retrieval models,” Proc. of the 40th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 515-524, 2017.
8. [8] D.-K. Chae et al., “CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks,” Proc. of the 27th ACM Int. Conf. on Information and Knowledge Management, pp. 137-146, 2018.
9. [9] H. Wang et al., “Graphgan: Graph representation learning with generative adversarial nets,” arXiv preprint, arXiv:1711.08267, 2017.
10. [10] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint, arXiv:1609.02907, 2016.
11. [11] R. van den Berg et al., “Graph Convolutional Matrix Completion,” arXiv preprint, arXiv:1706.02263, 2017.
12. [12] R. Ying et al., “Graph Convolutional Neural Networks for Web-Scale Recommender Systems,” arXiv preprint, arXiv:1806.01973, 2018.
13. [13] M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint, arXiv:1411.1784, 2014.
14. [14] F. M. Harper and J. A. Konstan, “The MovieLens datasets: History and context,” ACM Trans. on Interactive Intelligent Systems, Vol.5, No.4, Article No.19, 2016.
15. [15] I. Goodfellow et al., “Deep Learning,” MIT Press, 2016.