Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and the other is to adopt the generative model Variational Auto-encoder (VAE). However, the existing works still face two challenges. First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns. Second, the VAE based models are not expressive enough to capture the true preference since VAE often yields an embedding space of a single modal, while in real world, user-item interactions usually exhibit multi-modality on user preference distribution. In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and capture the multi-modality of the embedding space, by combining the advantages of VAE and adversarial training between the introduced auxiliary discriminators and the variational inference networks. The extensive experiments conducted on real datasets verify the effectiveness of DAVE on robust recommendation.

中文翻译：

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稳健推荐的双重对抗变分嵌入

鲁棒推荐旨在从嘈杂的数据中捕获用户的真实偏好，为此已经提出了两种方法。一种是基于噪声注入，另一种是采用生成模型变分自动编码器（VAE）。然而，现有的工作仍然面临两个挑战。首先，基于噪声注入的方法通常从预先给定的固定噪声分布中提取噪声，而在现实世界中，由于个人行为和项目使用模式，不同用户和项目的噪声分布可能彼此不同。其次，基于 VAE 的模型的表现力不足以捕捉真正的偏好，因为 VAE 通常会产生单个模态的嵌入空间，而在现实世界中，用户-项目交互通常在用户偏好分布上表现出多模态。在本文中，我们提出了一种称为双对抗变分嵌入（DAVE）的新模型用于鲁棒推荐，它可以为不同的用户和项目提供个性化的降噪，并通过结合 VAE 和对抗训练之间的优势来捕捉嵌入空间的多模态引入的辅助鉴别器和变分推理网络。在真实数据集上进行的大量实验验证了 DAVE 在稳健推荐方面的有效性。通过在引入的辅助鉴别器和变分推理网络之间结合 VAE 和对抗训练的优势。在真实数据集上进行的大量实验验证了 DAVE 在稳健推荐方面的有效性。通过在引入的辅助鉴别器和变分推理网络之间结合 VAE 和对抗训练的优势。在真实数据集上进行的大量实验验证了 DAVE 在稳健推荐方面的有效性。