Composition pattern-aware web service recommendation based on depth factorisation machine

Abstract

Web service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services to develop Web applications that satisfy functional requirements. In order to recommend Web services considering user's preferences, a composition pattern-aware Web service recommendation method called EWACP-DeepFM is proposed, which combines the composition patterns between Web services and Mashups and the co-occurrence and popularity of Web services. By constructing a multi-dimensional feature matrix, which is further trained by the depth factorisation machine (DeepFM) model to learn potential link relationships between Web services and Mashup applications, and recommend Top-N best services for the target Mashup application. Experiments performed using the real datasets from ProgrammableWeb show that the proposed method outperforms others with better recommendation effectiveness.