Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growing interest in leveraging graph neural networks (GNNs) for recommender systems, capitalizing on advancements in graph representation learning. These GNN-based models primarily focus on analyzing users' positive feedback while overlooking the valuable insights provided by their negative feedback. In this paper, we propose PANE-GNN, an innovative recommendation model that unifies Positive And Negative Edges in Graph Neural Networks for recommendation. By incorporating user preferences and dispreferences, our approach enhances the capability of recommender systems to offer personalized suggestions. PANE-GNN first partitions the raw rating graph into two distinct bipartite graphs based on positive and negative feedback. Subsequently, we employ two separate embeddings, the interest embedding and the disinterest embedding, to capture users' likes and dislikes, respectively. To facilitate effective information propagation, we design distinct message-passing mechanisms for positive and negative feedback. Furthermore, we introduce a distortion to the negative graph, which exclusively consists of negative feedback edges, for contrastive training. This distortion plays a crucial role in effectively denoising the negative feedback. The experimental results provide compelling evidence that PANE-GNN surpasses the existing state-of-the-art benchmark methods across four real-world datasets. These datasets include three commonly used recommender system datasets and one open-source short video recommendation dataset.

翻译：推荐系统通过向用户提供个性化推荐，在应对信息过载问题中发挥着关键作用。近年来，利用图神经网络（GNN）推进图表示学习，并将其应用于推荐系统引起了广泛关注。然而，这些基于GNN的模型主要聚焦于分析用户的正反馈，而忽略了负反馈所蕴含的宝贵信息。本文提出PANE-GNN，一种在推荐图神经网络中融合正边与负边的创新推荐模型。通过整合用户偏好与厌恶信息，我们的方法提升了推荐系统提供个性化建议的能力。PANE-GNN首先将原始评分图根据正负反馈分割为两个不同的二分图，随后分别采用兴趣嵌入与不感兴趣嵌入来捕捉用户的好恶。为促进有效的信息传播，我们设计了针对正反馈与负反馈的差异化消息传递机制。此外，我们对仅包含负反馈边的负图引入扭曲操作以进行对比训练——该扭曲在有效去噪负反馈中起着关键作用。实验结果表明，在四个真实世界数据集（包含三个常用推荐系统数据集与一个开源短视频推荐数据集）上，PANE-GNN显著超越了现有最先进的基准方法。