Dynamic Service Recommendation Using Lightweight BERT-based Service Embedding in Edge Computing

With the rapid development of the Internet of Things (IoT) as well as edge computing, and fog computing, many microservices are being created. Service recommendation based on these distributed environments is an important issue for boosting the utilization of services since service composition in edge and cloud computing has increasingly attracted attention. However, the direct application of traditional service recommendation methods in edge computing encounters several problems such as insufficient computing resources, and the dynamic update of recommendation systems. This paper presents a deep learning-based approach for dynamic service recommendations using lightweight BERT-based service embedding to address the problems. First, a lightweight BERT-based service embedding was proposed to learn the practical-value vector of service based on the invocation association. Second, based on service embedding, a content-based filtering method is utilized to perform service recommendations. Next, a dynamic update process is implemented on the system by fine-tuning the model. Finally, the experimental results show that our approach can perform service recommendations effectively.