AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations

Collaborative filtering methods based on graph neural networks (GNNs) havewitnessed significant success in recommender systems (RS), capitalizing ontheir ability to capture collaborative signals within intricate user-itemrelationships via message-passing mechanisms. However, these GNN-based RSinadvertently introduce excess linear correlation between user and itemembeddings, contradicting the goal of providing personalized recommendations.While existing research predominantly ascribes this flaw to the over-smoothingproblem, this paper underscores the critical, often overlooked role of theover-correlation issue in diminishing the effectiveness of GNN representationsand subsequent recommendation performance. Up to now, the over-correlationissue remains unexplored in RS. Meanwhile, how to mitigate the impact ofover-correlation while preserving collaborative filtering signals is asignificant challenge. To this end, this paper aims to address theaforementioned gap by undertaking a comprehensive study of the over-correlationissue in graph collaborative filtering models. Firstly, we present empiricalevidence to demonstrate the widespread prevalence of over-correlation in thesemodels. Subsequently, we dive into a theoretical analysis which establishes apivotal connection between the over-correlation and over-smoothing issues.Leveraging these insights, we introduce the Adaptive Feature De-correlationGraph Collaborative Filtering (AFDGCF) framework, which dynamically appliescorrelation penalties to the feature dimensions of the representation matrix,effectively alleviating both over-correlation and over-smoothing issues. Theefficacy of the proposed framework is corroborated through extensiveexperiments conducted with four representative graph collaborative filteringmodels across four publicly available datasets.