PRGCL: Path Reconstruction for Graph Contrastive Learning in Collaborative Filtering

Graph contrastive learning (GCL) has recently emerged as an effective self-supervised enhancement paradigm for alleviating data sparsity. However, existing methods still face three key challenges: (1) Stochastic structural perturbations may compromise local structure, making the constructed views unreliable; (2) The mixing of frequency domain signals can cause noise interference, leading to ambiguous optimization objectives; (3) Independently constructed views may induce different connectivity in the signal propagation path. To address these challenges, we propose a coupled reconstruction principle that leverages path reconstruction to identify task relevant structural subsets, guiding self-supervised view enhancement for graph collaborative filtering. Building on this, we propose Path Reconstruction for Graph Contrastive Learning in collaborative filtering (PRGCL), which improves graph contrastive recommendation by constructing reliable self-supervised views, refining frequency domain signals, and enforcing connectivity along signal propagation paths. Extensive experiments on three highly sparse real-world datasets demonstrate that PRGCL consistently outperforms state-of-the-art baselines in recommendation accuracy.