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. While many existing models in CF incorporate these methods in their design%2C there seems to be a limited depth of analysis regarding the foundational principles behind them. This paper bridges graph convolution%2C a pivotal element of graph-based models%2C with contrastive learning through a theoretical framework. By examining the learning dynamics and equilibrium of the contrastive loss%2C we offer a fresh lens to understand contrastive learning via graph theory%2C emphasizing its capability to capture high-order connectivity. Building on this analysis%2C we further show that the graph convolutional layers often used in graph-based models are not essential for high-order connectivity modeling and might contribute to the risk of oversmoothing. Stemming from our findings%2C we introduce Simple Contrastive Collaborative Filtering (SCCF)%2C a simple and effective algorithm based on a naive embedding model and a modified contrastive loss. The efficacy of the algorithm is demonstrated through extensive experiments across four public datasets. The experiment code is available at url{https%3A%2F%2Fgithub.com%2Fwu1hong%2FSCCF}. end{abstract}?quality=80&w=800)
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