Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling the sparse data problem of recommendation systems. However, existing contrastive learning methods still have limitations in resisting noise interference, especially for multi-behavior recommendation. To mitigate the aforementioned issues, this paper proposes a GNN-based multi-behavior recommendation model called MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences across different behaviors, improving recommendation effectiveness. First, MB-SVD integrates the representation of users and items under different behaviors with learnable weight scores, which efficiently considers the influence of different behaviors. Then, MB-SVD generates augmented graph representation with global collaborative relations. Next, we simplify the contrastive learning framework by directly contrasting original representation with the enhanced representation using the InfoNCE loss. Through extensive experimentation, the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets is exhibited.

翻译：图神经网络（GNNs）已被广泛应用于推荐系统领域，为用户提供个性化推荐并取得了显著成果。近年来，融入对比学习的GNNs在解决推荐系统的稀疏数据问题方面展现出良好性能。然而，现有对比学习方法在抵抗噪声干扰方面仍存在局限性，尤其在多行为推荐场景中。为缓解上述问题，本文提出一种基于GNN的多行为推荐模型MB-SVD，该模型利用奇异值分解（SVD）图来增强模型性能。具体而言，MB-SVD考虑了用户在不同行为下的偏好，从而提升推荐效果。首先，MB-SVD通过可学习权重分数整合用户和物品在不同行为下的表示，有效考虑了不同行为的影响。接着，MB-SVD生成具有全局协同关系的增强图表示。然后，我们简化对比学习框架，直接使用InfoNCE损失对比原始表示与增强表示。通过大量实验，我们提出的MB-SVD方法在多种真实数据集上的多行为推荐任务中展现出卓越性能。