统一自监督与监督学习，提升图推荐模型训练速度与效果
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
- 将推荐与对比学习融合为单一监督对比损失
- 在三个数据集上准确率优于现有方法，训练更快
- 适合需要高效训练的工业级推荐系统场景
推荐系统在在线平台中至关重要，能从海量信息中为用户提供个性化建议。自监督图学习通过用户-物品二分图的无监督增强，挖掘高阶协同过滤信号，通常采用包含监督推荐损失和自监督对比损失的多任务框架。然而，这种分离设计引入额外的图卷积过程，且因损失差异导致梯度方向不一致，造成训练时间长、性能不佳。本文提出统一的监督图对比学习框架（SGCL），将推荐与无监督对比损失整合为单一监督对比损失，使两项任务共享统一优化方向，实现极快训练。在三个真实数据集上的大量实验表明，SGCL显著优于现有最先进方法，在准确率和效率上均表现更优。
原文摘要 · Abstract (English)
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filtering signals through unsupervised augmentation on the user-item bipartite graph, primarily leveraging a multi-task learning framework that includes both supervised recommendation loss and self-supervised contrastive loss. However, this separate design introduces additional graph convolution processes and creates inconsistencies in gradient directions due to disparate losses, resulting in prolonged training times and sub-optimal performance. In this study, we introduce a unified framework of Supervised Graph Contrastive Learning for recommendation (SGCL) to address these issues. SGCL uniquely combines the training of recommendation and unsupervised contrastive losses into a cohesive supervised contrastive learning loss, aligning both tasks within a single optimization direction for exceptionally fast training. Extensive experiments on three real-world datasets show that SGCL outperforms state-of-the-art methods, achieving superior accuracy and efficiency.
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