  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.
