NPGCL: neighbor enhancement and embedding perturbation with graph contrastive learning for recommendation

Graph Neural Networks (GNNs) have significantly advanced recommendation systems by modeling user-item interactions through bipartite graphs. However, real-world user-item interaction data are often sparse and noisy. Traditional bipartite graph modeling fails to capture higher-order relationships between users and items, limiting the ability of GNNs to learn high-quality node embeddings. While existing graph contrastive learning methods address data sparsity by partitioning nodes into positive and negative pairs, they also neglect these higher-order relationships, thus limiting the effectiveness of contrastive learning in recommendation systems. Furthermore, due to the inherent limitations of graph convolution, noise can propagate and amplify with increasing layers in deep graph convolutional networks. To address these challenges, Neighbor Enhancement and Embedding Perturbation for Graph Contrastive Learning (NPGCL) is proposed, which introduces two key modules - Relational Neighbor Enhancement Module and Collaborative Neighbor Enhancement Module - to capture higher-order relationships between homogeneous nodes and calculate interaction importance for noise suppression. Moreover, NPGCL employs an Embedding Perturbation Strategy and applies inter-layer contrastive learning to mitigate the noise impact caused by multi-layer graph convolutions. Experimental results demonstrate that NPGCL significantly improves performance across four publicly available datasets, with a notable enhancement in robustness, especially in noisy environments. Specifically, NPGCL achieves performance improvements of 1.77%-3.34% and 3.87%-9.07% on the Gowalla and Amazon-books datasets, respectively. In noisy datasets, NPGCL improves Recall@20 by 4.98% and 10.92%, respectively.