论文标题
WGAN-GP在建议和质疑基于GAN的方法的相关性中的应用
Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approaches
论文作者
论文摘要
近年来提出了许多基于神经的推荐系统，其中一部分使用生成的对抗网络（GAN）来对用户项目的交互进行建模。但是，根据建议，对以梯度罚款（WGAN-GP）的Wasserstein Gan进行了相对较少的审查。在本文中，我们关注两个问题：1-我们可以成功地将WGAN-GP应用于建议吗？与最佳GAN模型相比，这种方法是否具有优势？ 2-基于GAN的推荐系统是否相关？为了回答第一个问题，我们提出了一个基于wgan-GP的推荐系统，该系统称为CFWGAN-GP，该系统建立在先前的模型（CFGAN）上。我们成功地将我们的方法应用于TOP-K推荐任务上的现实数据集，而经验结果表明，它与最先进的GAN方法具有竞争力，但是我们没有发现使用WGAN-GP而不是原始GAN的显着优势，至少从准确的角度来看。至于第二个问题，我们进行了一个简单的实验，我们表明，在概念上更简单的方法通过相当大的余量优于基于GAN的模型，从而质疑这种模型的使用。
Many neural-based recommender systems were proposed in recent years and part of them used Generative Adversarial Networks (GAN) to model user-item interactions. However, the exploration of Wasserstein GAN with Gradient Penalty (WGAN-GP) on recommendation has received relatively less scrutiny. In this paper, we focus on two questions: 1- Can we successfully apply WGAN-GP on recommendation and does this approach give an advantage compared to the best GAN models? 2- Are GAN-based recommender systems relevant? To answer the first question, we propose a recommender system based on WGAN-GP called CFWGAN-GP which is founded on a previous model (CFGAN). We successfully applied our method on real-world datasets on the top-k recommendation task and the empirical results show that it is competitive with state-of-the-art GAN approaches, but we found no evidence of significant advantage of using WGAN-GP instead of the original GAN, at least from the accuracy point of view. As for the second question, we conduct a simple experiment in which we show that a well-tuned conceptually simpler method outperforms GAN-based models by a considerable margin, questioning the use of such models.
