@inproceedings{67b28c455d694bcdbc50c1139034e4bd,
title = "RsyGAN: Generative Adversarial Network for Recommender Systems",
abstract = "Many recommender systems rely on the information of user-item interactions to generate recommendations. In real applications, the interaction matrix is usually very sparse, as a result, the model cannot be optimised stably with different initial parameters and the recommendation performance is unsatisfactory. Many works attempted to solve this problem, however, the parameters in their models may not be trained effectively due to the sparse nature of the dataset which results in a lower quality local optimum. In this paper, we propose a generative network for making user recommendations and a discriminative network to guide the training process. An adversarial training strategy is also applied to train the model. Under the guidance of a discriminative network, the generative network converges to an optimal solution and achieves better recommendation performance on a sparse dataset. We also show that the proposed method significantly improves the precision of the recommendation performance on several datasets.",
keywords = "component, formatting, insert, style, styling",
author = "Ruiping Yin and Kan Li and Jie Lu and Guangquan Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",
year = "2019",
month = jul,
doi = "10.1109/IJCNN.2019.8851727",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 International Joint Conference on Neural Networks, IJCNN 2019",
address = "United States",
}