LLM-CoSR: Noise-Resistant Service Recommendation via LLM-Augmented Graph Contrastive Learning

Service recommendation is an essential task in service computing that helps users discover and select the most suitable services from a vast pool of available options. With the rapid growth of service-oriented architectures and cloud computing, the number of services has increased exponentially, making automated service selection crucial. While service descriptions and invocation networks provide valuable preliminary information for service recommendation, current research largely overlooks the anomalous behaviors in these networks, such as invoking dead services or measuring service similarity improperly. These anomalies introduce noise that can lead to lowquality embeddings. Such behaviors are difficult to eliminate and ultimately degrade recommendation performance. To address this challenge, we propose Noise Resistant Service Recommendation via LLM-Augmented Contrastive Learning (LLM-CoSR), a robust model that leverages Large Language Models (LLMs) for service recommendation. Our approach uses graph contrastive learning (GCL) to separate normal and anomalous services in the representation space while clustering normal services together. The invocation network variants in GCL are refined by LLMs through carefully designed prompts, enabling LLMCoSR to resist anomalous behaviors in the invocation network and generate more robust recommendations. Experimental evaluations on the ProgrammableWeb dataset demonstrate that LLMCoSR achieves superior performance compared to state-of-theart (SOTA) methods. The source code and parameter configurations are available at: https://github.com/catwinee/LLM-CoSR.