The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike, low rating) that widely exist in real-world recommender systems. How to utilize negative feedback in graph-based recommendations still remains underexplored. In this study, we first conducted a comprehensive experimental analysis and found that (1) existing graph neural networks are not well-suited for modeling negative feedback, which acts as a high-frequency signal in a user-item graph. (2) The graph-based recommendation suffers from the representation degeneration problem. Based on the two observations, we propose a novel model that models positive and negative feedback from a frequency filter perspective called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN). Specifically, in DFGNN, the designed dual-frequency graph filter (DGF) captures both low-frequency and high-frequency signals that contain positive and negative feedback. Furthermore, the proposed signed graph regularization is applied to maintain the user/item embedding uniform in the embedding space to alleviate the representation degeneration problem. Additionally, we conduct extensive experiments on real-world datasets and demonstrate the effectiveness of the proposed model. Codes of our model will be released upon acceptance.

翻译：基于图的推荐系统近年来取得了巨大成功。然而，现有的大多数基于图的推荐方法主要关注基于正边/正反馈（如喜欢、高评分）来捕捉用户偏好，而忽略了现实推荐系统中广泛存在的负边/负反馈（如不喜欢、低评分）。如何在基于图的推荐中有效利用负反馈仍是一个尚未充分探索的问题。在本研究中，我们首先进行了全面的实验分析，发现：（1）现有的图神经网络并不适合建模在用户-物品图中表现为高频信号的负反馈；（2）基于图的推荐存在表示退化问题。基于这两点观察，我们提出了一种从频率滤波视角建模正负反馈的新模型——面向符号感知推荐的双频图神经网络（DFGNN）。具体而言，在DFGNN中，所设计的双频图滤波器（DGF）能够同时捕获包含正反馈的低频信号与包含负反馈的高频信号。此外，所提出的符号图正则化被应用于保持用户/物品嵌入在嵌入空间中的均匀分布，以缓解表示退化问题。我们在多个真实数据集上进行了广泛的实验，验证了所提出模型的有效性。模型代码将在论文录用后公开。