Recently, malevolent user hacking has become a huge problem for real-world companies. In order to learn predictive models for recommender systems, factorization techniques have been developed to deal with user-item ratings. In this paper, we suggest a broad architecture of a factorization model with adversarial training to get over these issues. The effectiveness of our systems is demonstrated by experimental findings on real-world datasets.

翻译：最近,恶意用户黑客入侵已成为现实世界公司的一个大问题。 为了学习推荐人系统的预测模型,已经开发了处理用户项目评级的计数技术。 在本文中,我们建议了一种带有对抗性培训的乘数模型的广泛结构,以克服这些问题。我们系统的有效性表现在现实世界数据集的实验性发现上。