用于推荐的混合监督图对比学习
Mixed Supervised Graph Contrastive Learning for Recommendation
AI总结：
提出MixSGCL方法，将推荐与对比损失整合为监督对比学习损失以统一优化方向，并引入节点级和边级混合挖掘监督信号，有效缓解数据稀疏性，在准确率和效率上超越现有方法。
摘要：
推荐系统（RecSys）在在线平台中发挥着至关重要的作用，在海量信息中为用户提供个性化建议。图对比学习旨在通过在用户-项目二部图上进行无监督增强来学习高阶协同过滤信号，这主要依赖于包含成对推荐损失和对比损失的多任务学习框架。这种解耦设计会导致不同损失产生不一致的优化方向，从而导致更长的收敛时间甚至次优性能。此外，自监督对比损失在缓解推荐系统中的数据稀疏性问题方面存在不足，因为它学习区分不同视图的用户/项目，而在增强过程中没有提供额外的监督协同过滤信号。在本文中，我们提出了用于推荐的混合监督图对比学习（MixSGCL）来解决这些问题。MixSGCL创新性地将推荐和无监督对比损失的训练整合到一个监督对比学习损失中，使两个任务在同一优化方向上对齐。为了应对数据稀疏性问题，我们进一步提出了节点级和边级混合，基于现有的用户-项目交互挖掘更直接的监督协同过滤信号，而不是无监督增强。在三个真实世界数据集上的大量实验表明，MixSGCL超越了最先进的方法，在准确性和效率上均达到了顶级性能。这验证了MixSGCL在监督图对比学习上的耦合设计的有效性。
英文摘要：
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss. This decoupled design can cause inconsistent optimization direction from different losses, which leads to longer convergence time and even sub-optimal performance. Besides, the self-supervised contrastive loss falls short in alleviating the data sparsity issue in RecSys as it learns to differentiate users/items from different views without providing extra supervised collaborative filtering signals during augmentations. In this paper, we propose Mixed Supervised Graph Contrastive Learning for Recommendation (MixSGCL) to address these concerns. MixSGCL originally integrates the training of recommendation and unsupervised contrastive losses into a supervised contrastive learning loss to align the two tasks within one optimization direction. To cope with the data sparsity issue, instead unsupervised augmentation, we further propose node-wise and edge-wise mixup to mine more direct supervised collaborative filtering signals based on existing user-item interactions. Extensive experiments on three real-world datasets demonstrate that MixSGCL surpasses state-of-the-art methods, achieving top performance on both accuracy and efficiency. It validates the effectiveness of MixSGCL with our coupled design on supervised graph contrastive learning.
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。
