Graph Neural Networks (GNNs) has been extensively employed in the field of recommender systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling sparse data problem of recommendation system. However, existing contrastive learning methods still have limitations in addressing the cold-start problem and resisting noise interference especially for multi-behavior recommendation. To mitigate the aforementioned issues, the present research posits a GNNs based multi-behavior recommendation model MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences under different behaviors, improving recommendation effectiveness while better addressing the cold-start problem. Our model introduces an innovative methodology, which subsume multi-behavior contrastive learning paradigm to proficiently discern the intricate interconnections among heterogeneous manifestations of user behavior and generates SVD graphs to automate the distillation of crucial multi-behavior self-supervised information for robust graph augmentation. Furthermore, the SVD based framework reduces the embedding dimensions and computational load. Thorough experimentation showcases the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets.

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效。近年来，融合对比学习的GNNs在处理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解决冷启动问题和抵抗噪声干扰方面仍存在局限，尤其是针对多行为推荐场景。为缓解上述问题，本研究提出一种基于GNNs的多行为推荐模型MB-SVD，利用奇异值分解（SVD）图来增强模型性能。具体而言，MB-SVD考虑了不同行为下的用户偏好，在提升推荐效果的同时更好地解决了冷启动问题。该模型引入了一种创新方法，包含多行为对比学习范式，能有效识别用户行为异质表现之间的复杂相互关联，并生成SVD图以自动提炼关键的多行为自监督信息，实现稳健的图增强。此外，基于SVD的框架降低了嵌入维度和计算开销。充分的实验表明，所提出的MB-SVD方法在多个真实世界数据集上的多行为推荐任务中展现出卓越性能。