With the rapid growth of cloud services driven by advancements in web service technology, selecting a high-quality service from a wide range of options has become a complex task. This study aims to address the challenges of data sparsity and the cold-start problem in web service recommendation using Quality of Service (QoS). We propose a novel approach called QoS-aware graph contrastive learning (QAGCL) for web service recommendation. Our model harnesses the power of graph contrastive learning to handle cold-start problems and improve recommendation accuracy effectively. By constructing contextually augmented graphs with geolocation information and randomness, our model provides diverse views. Through the use of graph convolutional networks and graph contrastive learning techniques, we learn user and service embeddings from these augmented graphs. The learned embeddings are then utilized to seamlessly integrate QoS considerations into the recommendation process. Experimental results demonstrate the superiority of our QAGCL model over several existing models, highlighting its effectiveness in addressing data sparsity and the cold-start problem in QoS-aware service recommendations. Our research contributes to the potential for more accurate recommendations in real-world scenarios, even with limited user-service interaction data.

翻译：随着Web服务技术发展推动云服务快速增长，如何从海量服务中选择高质量服务已成为复杂任务。本研究旨在利用服务质量（QoS）解决Web服务推荐中的数据稀疏性和冷启动问题。我们提出了一种名为QoS感知图对比学习（QAGCL）的Web服务推荐新方法。该模型充分利用图对比学习的优势，有效处理冷启动问题并提升推荐准确性。通过构建融合地理位置信息与随机性的上下文增强图，模型获得多样化视图。结合图卷积网络与图对比学习技术，我们从这些增强图中学习用户与服务的嵌入表示。所学习的嵌入表示能够将QoS考量无缝融入推荐流程。实验结果表明，我们的QAGCL模型在多个现有模型中表现优异，有效解决了QoS感知服务推荐中的数据稀疏性与冷启动问题。本研究的贡献在于，即便在用户-服务交互数据有限的情况下，仍能实现更精准的现实场景推荐。