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

Abstract

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 a 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. The unclear relationship between over-correlation and over-smoothing in RS, coupled with the challenge of adaptively minimizing the impact of over-correlation while preserving collaborative filtering signals, is quite challenging. 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. Empirical evidence substantiates the widespread prevalence of over-correlation in these models. Furthermore, a theoretical analysis establishes a pivotal connection between the over-correlation and over-smoothing predicaments. 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 challenges. The efficacy of the proposed framework is corroborated through extensive experiments conducted with four different graph collaborative filtering models across four publicly available datasets, demonstrating the superiority of AFDGCF in enhancing the performance landscape of graph collaborative filtering models.