Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this flaw to the over-smoothing problem, this paper underscores the critical, often overlooked role of the over-correlation issue in diminishing the effectiveness of GNN representations and subsequent recommendation performance. Up to now, the over-correlation issue remains unexplored in RS. Meanwhile, how to mitigate the impact of over-correlation while preserving collaborative filtering signals is a significant challenge. To this end, this paper aims to address the aforementioned gap by undertaking a comprehensive study of the over-correlation issue in graph collaborative filtering models. Firstly, we present empirical evidence to demonstrate the widespread prevalence of over-correlation in these models. Subsequently, we dive into a theoretical analysis which establishes a pivotal connection between the over-correlation and over-smoothing issues. Leveraging these insights, we introduce the Adaptive Feature De-correlation Graph Collaborative Filtering (AFDGCF) framework, which dynamically applies correlation penalties to the feature dimensions of the representation matrix, effectively alleviating both over-correlation and over-smoothing issues. The efficacy of the proposed framework is corroborated through extensive experiments conducted with four representative graph collaborative filtering models across four publicly available datasets.

翻译：基于图神经网络（GNN）的协同过滤方法通过消息传递机制捕捉复杂用户-物品关系中的协同信号，已在推荐系统中取得显著成功。然而，此类基于GNN的推荐系统会无意中引入用户嵌入与物品嵌入之间的线性相关性，这与提供个性化推荐的目标相悖。现有研究通常将此缺陷归因于过平滑问题，但本文强调被忽视的过相关性问题对削弱GNN表征有效性及后续推荐性能的关键作用。截至目前，过相关性问题在推荐系统中仍未被探索。同时，如何在保留协同过滤信号的同时缓解过相关性的影响是一个重大挑战。为此，本文旨在通过对图协同过滤模型中的过相关性问题进行系统性研究来填补上述空白。首先，我们通过实验证据展示过相关性在这些模型中的普遍存在性。其次，我们深入进行理论分析，建立过相关性与过平滑问题之间的关键联系。基于这些见解，我们提出自适应特征去相关图协同过滤（AFDGCF）框架，该框架动态地对表征矩阵的特征维度施加相关性惩罚，有效缓解过相关性与过平滑问题。通过在四个公开数据集上使用四种代表性图协同过滤模型进行的广泛实验，验证了所提出框架的有效性。