Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering

• URL: http://arxiv.org/abs/2406.13996v2
• Date: Fri, 21 Jun 2024 04:46:59 GMT
• Title: Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
• Authors: Yihong Wu, Le Zhang, Fengran Mo, Tianyu Zhu, Weizhi Ma, Jian-Yun Nie,
• Abstract summary: Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering. This paper bridges graph convolution, a pivotal element of graph-based models, with contrastive learning through a theoretical framework. We introduce Simple Contrastive Collaborative Filtering (SCCF), a simple and effective algorithm based on a naive embedding model and a modified contrastive loss.
• Score: 33.4238287258316
• License:
• Abstract: Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their design, there seems to be a limited depth of analysis regarding the foundational principles behind them. This paper bridges graph convolution, a pivotal element of graph-based models, with contrastive learning through a theoretical framework. By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity. Building on this analysis, 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, we introduce Simple Contrastive Collaborative Filtering (SCCF), 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://github.com/wu1hong/SCCF}. \end{abstract}

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