你的图推荐系统可证明是一个单视图图对比学习
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning
AI总结：
本文从编码器和损失函数角度证明图推荐系统等价于单视图图对比学习模型，发现推荐损失与GCL损失可互换使用，为跨领域研究提供新视角。
摘要：
图推荐系统（GR）是一种定制的图神经网络（GNNs）编码器，用于从用户-项目交互图中提取信息。由于其在推荐任务上的强大性能，GR最近获得了显著关注。图对比学习（GCL）也是一个受欢迎的研究方向，旨在通过特定的对比目标学习（通常是无监督的）GNNs。作为一种通用的图表示学习方法，GCL被广泛采用，与监督推荐损失联合训练GR。尽管GR和GCL研究存在交叉，但对这两个领域之间关系的理论理解却惊人地稀少。这一空白不可避免地导致科学研究效率低下。在本文中，我们旨在从编码器和损失函数的角度弥合GR和GCL领域之间的差距。在温和的假设下，我们在理论上展示了一个惊人的事实：图推荐系统等价于一个常用的单视图图对比模型。具体而言，我们发现（1）GR中的经典编码器本质上是一个带独热输入的线性图卷积网络，且（2）GR中的损失函数被具有特定超参数的单视图GCL损失良好界定。第一个观察使我们能解释GR模型的关键设计，例如自环和非线性的移除。第二个发现能轻易催生许多跨领域研究方向。我们实证展示了一个显著结果：推荐损失和GCL损失可互换使用。仅用GCL损失训练GR模型这一事实特别有启发性，因为在此工作之前，GCL通常被视为需要微调的无监督方法。我们还讨论了受我们理论启发的一些潜在未来工作。
英文摘要：
Graph recommender (GR) is a type of graph neural network (GNNs) encoder that is customized for extracting information from the user-item interaction graph. Due to its strong performance on the recommendation task, GR has gained significant attention recently. Graph contrastive learning (GCL) is also a popular research direction that aims to learn, often unsupervised, GNNs with certain contrastive objectives. As a general graph representation learning method, GCLs have been widely adopted with the supervised recommendation loss for joint training of GRs. Despite the intersection of GR and GCL research, theoretical understanding of the relationship between the two fields is surprisingly sparse. This vacancy inevitably leads to inefficient scientific research. In this paper, we aim to bridge the gap between the field of GR and GCL from the perspective of encoders and loss functions. With mild assumptions, we theoretically show an astonishing fact that graph recommender is equivalent to a commonly-used single-view graph contrastive model. Specifically, we find that (1) the classic encoder in GR is essentially a linear graph convolutional network with one-hot inputs, and (2) the loss function in GR is well bounded by a single-view GCL loss with certain hyperparameters. The first observation enables us to explain crucial designs of GR models, e.g., the removal of self-loop and nonlinearity. And the second finding can easily prompt many cross-field research directions. We empirically show a remarkable result that the recommendation loss and the GCL loss can be used interchangeably. The fact that we can train GR models solely with the GCL loss is particularly insightful, since before this work, GCLs were typically viewed as unsupervised methods that need fine-tuning. We also discuss some potential future works inspired by our theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
