Improving graph-based recommendation with unraveled graph learning

Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph Neural Networks. Furthermore, the integration of contrastive learning has enhanced the performance of GraphCF methods. Recent research has shifted from graph augmentation to noise perturbation in contrastive learning, leading to significant performance improvements. However, we contend that the primary factor in performance enhancement is not graph augmentation or noise perturbation, but rather the balance of the embedding from each layer in the output embedding. To substantiate our claim, we conducted preliminary experiments with multiple state-of-the-art GraphCF methods. Based on our observations and insights, we propose a novel approach named Unraveled Graph Contrastive Learning (UGCL), which includes a new propagation scheme to further enhance performance. To the best of our knowledge, this is the first approach that specifically addresses the balance factor in the output embedding for performance improvement. We have carried out extensive experiments on multiple large-scale benchmark datasets to evaluate the effectiveness of our proposed approach. The results indicate that UGCL significantly outperforms all other state-of-the-art baseline models, also showing superior performance in terms of fairness and debiasing capabilities compared to other baselines.