DySR: A Dynamic Graph Neural Network Based Service Bundle Recommendation Model for Mashup Creation

An increasing number and diversity of services are available, which results in significant challenges to effectively reuse service during mashup creation. Many works have modeled the mashup creation problem as a service recommendation task and have achieved remarkable results. However, the performance of these methods can be further improved. The main problems affecting these methods include the constraints among recommended services, the evolution of services, and the semantic gap existing in services and mashups. In this article, we model the mashup creation problem as a service bundle recommendation task that is formally defined to address the constraints among recommended services. And then, a dynamic graph neural network based model called DySR is proposed to tackle the evolution of service and the semantic gap between services and mashups. In order to quantitatively measure how significant the semantic gap between mashups and services is, a measurement method of a semantic gap is given. With it, experiments show that to what extent DySR can reduce the semantic gap in the context of mashup creation. In addition, new evaluation metrics are introduced to overcome the preference for popular services in traditional service recommendations. Extensive experiments conducted on a real-world dataset from ProgrammableWeb, and the experiment results show that DySR outperforms existing state-of-the-art methods.