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Graph-Based Diffusion Model for Service Recommendation
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AI
DOI：10.1109/TSC.2025.3646723.png)
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Abstract
En 中文
With the widespread adoption of cloud-based services and Service-Oriented Computing, efficient service recommendation has become pivotal for optimizing service discovery and composition in large-scale ecosystems. While recent diffusion-based recommendation methods have achieved impressive results in service-oriented scenarios, existing approaches predominantly treat user-service interactions as isolated events, overlooking the potential of higher-order collaborative signals between users and services. Such signals, which encapsulate richer and more nuanced relationships, can be naturally captured using graph-based data structures. To address this limitation, we extend diffusion-based service recommendation methods to the graph domain by directly modeling user-service bipartite graphs with diffusion models. This enables better modeling of the higher-order connectivity inherent in complex interaction dynamics. However, this extension introduces two primary challenges: (1) Noise Heterogeneity, where interactions are influenced by various forms of continuous and discrete noise, and (2) Relation Explosion, referring to the high computational costs of processing large-scale graphs. To tackle these challenges, we propose a Graph-based Diffusion Model for Service Recommendation (GDMSR). To address noise heterogeneity, we introduce a multi-level noise corruption mechanism that integrates both continuous and discrete noise, effectively simulating real-world interaction complexities. To mitigate relation explosion, we design a user-active guided diffusion process that selectively focuses on the high-value edges and active users, reducing inference costs while preserving critical service-level dependencies. Extensive experiments on six real-world service datasets demonstrate that GDMSR consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing higher-order collaborative signals and improving service recommendation performance.
Keywords:
Generative service recommendation
diffusion model
graph neural networks
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