While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals to address this. However, this approach often unintentionally distances the target user/item from their collaborative neighbors, limiting its efficacy. In response, we propose a solution that treats the collaborative neighbors of the anchor node as positive samples within the final objective loss function. This paper focuses on developing two unique supervised contrastive loss functions that effectively combine supervision signals with contrastive loss. We analyze our proposed loss functions through the gradient lens, demonstrating that different positive samples simultaneously influence updating the anchor node's embeddings. These samples' impact depends on their similarities to the anchor node and the negative samples. Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model. Our proposed Neighborhood-Enhanced Supervised Contrastive Loss (NESCL) model substitutes the contrastive loss function in SGL with our novel loss function, showing marked performance improvement. On three real-world datasets, Yelp2018, Gowalla, and Amazon-Book, our model surpasses the original SGL by 10.09%, 7.09%, and 35.36% on NDCG@20, respectively.

翻译：尽管协同过滤技术在推荐任务中表现有效，但其面临数据稀疏性的挑战。研究者开始利用对比学习引入额外的自监督信号以解决该问题，然而这一方法常会意外地拉远目标用户/项目与其协同邻居的距离，从而限制其效能。为此，我们提出将锚节点的协同邻居作为最终目标损失函数中的正样本的解决方案。本文聚焦于设计两种独特的监督对比损失函数，将监督信号与对比损失有效结合。通过梯度视角分析所提出的损失函数，我们发现不同正样本会同时影响锚节点嵌入的更新，且其影响程度取决于它们与锚节点及负样本的相似度。以基于图的协同过滤模型为骨干网络，并采用与现有对比学习模型SGL相同的数据增强方法，我们有效提升了推荐模型的性能。所提出的邻域增强监督对比损失（NESCL）模型通过用新颖的损失函数替代SGL中的对比损失函数，展现出显著的性能提升。在Yelp2018、Gowalla和Amazon-Book三个真实数据集上，我们的模型在NDCG@20指标上分别比原始SGL提高了10.09%、7.09%和35.36%。