Globally Informed Graph Contrastive Learning for Recommendation

In recent years, the fusion of graph convolutional networks (GCNs) with contrastive learning (CL) has emerged as a promising approach in recommender systems, owing to its adeptness in extracting self-supervised signals from the original data, thus addressing the challenge of data sparsity. While this methodology has been proven to be effective, it predominantly focuses on capturing discrepancies in user preferences through the construction of contrastive views, often neglecting the underlying commonalities in user preferences. To address this limitation, we introduce a novel framework, Globally Informed Graph Contrastive Learning (GIGCL), which aims to incorporate global features into the construction of contrastive views, facilitating the capture of shared features among nodes and refining the nodes' embedding outcomes. Central to our framework are four key modules, with the global features extractor module and the global informed fusion module serving as pivotal components. Following the initial embedding phase, local embeddings are fed into the global features extractor module to derive global features. Subsequently, in the global informed fusion module, the global features are seamlessly integrated with the local embeddings, generating a pair of contrastive views. Experimental results on two real-world datasets demonstrate the superiority of our proposed GIGCL over state-of-the-art baselines, and underscore the efficacy of GIGCL in reconciling divergent and convergent user preferences, thereby enhancing recommendation performance.