AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
<|reference_start|>AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations: 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.<|reference_end|>
@article{wu2024afdgcf:,
    title={AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering
  for Recommendations},
    author={Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong},
    journal={arXiv preprint arXiv:2403.17416},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17416},
    primaryClass={cs.IR}
}