DiffMSR: A Multi-Semantic Graph Diffusion Model for Service Recommendation

With the rapid development of cloud computing and service computing, service recommendation systems play a crucial role in helping users efficiently filter the appropriate services. However, the sparsity of service data and the presence of noise in interactions make it extremely challenging to accurately capture user preferences. Existing service recommendation methods based on Graph Neural Networks (GNNs) primarily rely on ID aggregation, often neglecting the richness of textual semantics and are susceptible to interaction noise, resulting in suboptimal modeling of user-service relationships. Although Large Language Models (LLMs) demonstrate remarkable advantages in capturing textual semantics, current methods struggle to effectively align structural representations with textual representations, limiting improvements in recommendation performance. To address these challenges, we propose an innovative multi-semantic graph diffusion model for service recommendation, DiffMSR, which aims to align textual and structural representations while learning the generation process of interaction graphs in a denoising manner. This approach mitigates data sparsity and effectively reduces noise interference. Specifically, the model leverages LLMs to capture the textual semantic features of service descriptions and integrates them with structured semantic information from knowledge graphs. Through cross-semantic contrastive learning, it achieves heterogeneous semantic alignment. Furthermore, the model introduces a multisemantic diffusion-based generation framework, which iteratively denoises to construct high-quality user-service interaction graphs. This significantly enhances the multi-semantic awareness of user representations, thereby improving recommendation performance. Experiments on public service datasets demonstrate that DiffMSR outperforms existing state-of-the-art baseline methods, achieving improvements of 4.13% and 6.37% in recommendation accuracy and recall, respectively.