What Is Next? A Generative Approach for Service Composition Recommendations

Abstract

Service recommendation is important in creating composite services, workflows, e-business solutions, etc. It often takes developers a long time to Figure out what the next service is. A lot of researchers have used collaborative filtering-based or content-based approaches to recommend services for developers. However, failing to model the co-occurrence relationships between services, current approaches cannot recommend the next services for service composition. This leads to a decrease in the accuracy of service composition recommendations. To tackle this problem, this paper proposes an Encoder-Decoder-based Recommender named EDeR, which transforms the service recommendation problem into a generation problem. First, we employ a self-supervised graph embedding method to fully learn the representation of each service according to both structural and descriptive information. Then, based on the co-occurrence relationships between services, we propose an encoder-decoder model to sequentially recommend services in a way that translates user requirements into a composite service. The results obtained from experiments conducted on a real-world dataset show that EDeR outperforms the state-of-the-art approaches significantly.