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获复杂用户-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，这类基于GNN的推荐系统会无意中引入用户嵌入与物品嵌入之间的过度线性相关性，这与提供个性化推荐的目标相悖。现有研究主要将这一缺陷归因于过平滑问题，但本文强调过相关问题在削弱GNN表征有效性及后续推荐性能中的关键作用——这一因素往往被忽视。截至目前，过相关问题在推荐系统中仍属未探索领域。同时，如何在保留协同过滤信号的同时减轻过相关的影响，仍是一项重大挑战。为此，本文旨在通过全面研究图协同过滤模型中的过相关问题来填补上述空白。首先，我们通过实证证据揭示了这些模型中过相关现象的普遍存在性。随后，我们深入理论分析，建立了过相关与过平滑问题之间的关键关联。基于这些洞见，我们提出自适应特征去相关图协同过滤（AFDGCF）框架，该框架对表征矩阵的特征维度动态施加相关性惩罚，有效缓解了过相关与过平滑问题。通过在四个公开数据集上对四种代表性图协同过滤模型进行广泛实验，验证了所提框架的有效性。