  Graph-based collaborative filtering (CF) has emerged as a promising approach
in recommendation systems. Despite its achievements, graph-based CF models face
challenges due to data sparsity and negative sampling. In this paper, we
propose a novel Stochastic sampling for i) COntrastive views and ii) hard
NEgative samples (SCONE) to overcome these issues. By considering that they are
both sampling tasks, we generate dynamic augmented views and diverse hard
negative samples via our unified stochastic sampling framework based on
score-based generative models. In our comprehensive evaluations with 6
benchmark datasets, our proposed SCONE significantly improves recommendation
accuracy and robustness, and demonstrates the superiority of our approach over
existing CF models. Furthermore, we prove the efficacy of user-item specific
stochastic sampling for addressing the user sparsity and item popularity
issues. The integration of the stochastic sampling and graph-based CF obtains
the state-of-the-art in personalized recommendation systems, making significant
strides in information-rich environments.
