PANE-GNN：在用于推荐的图神经网络中统一正边与负边
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
AI总结：
针对GNN推荐忽视负反馈的问题，本文提出PANE-GNN，通过正负二部图、兴趣/非兴趣嵌入、差异化消息传递及负图畸变对比训练，在四个真实数据集上超越现有基准。
摘要：
推荐系统通过向用户提供个性化推荐，在解决信息过载问题方面发挥着关键作用。近年来，利用图神经网络（GNN）来构建推荐系统受到越来越多的关注，这得益于图表示学习的进展。这些基于GNN的模型主要关注分析用户的正反馈，而忽视了用户负反馈所提供的有价值洞察。本文提出PANE-GNN，这是一种创新的推荐模型，在用于推荐的图神经网络中统一正边与负边（Positive And Negative Edges）。通过纳入用户的偏好与不偏好，该方法增强了推荐系统提供个性化建议的能力。PANE-GNN首先根据正反馈和负反馈，将原始评分图划分为两个不同的二部图。随后，我们采用两个相互独立的嵌入，即兴趣嵌入和非兴趣嵌入，分别捕捉用户的喜欢与不喜欢。为了促进有效的信息传播，我们为正反馈和负反馈设计了不同的消息传递机制。此外，我们对仅由负反馈边构成的负图引入一种畸变，用于对比训练。这种畸变在有效去除负反馈噪声方面发挥着关键作用。实验结果提供了有力证据，表明PANE-GNN在四个真实世界数据集上优于现有的最先进基准方法。这些数据集包括三个常用的推荐系统数据集和一个开源短视频推荐数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
