MSRDL: Deep learning framework for service recommendation in mashup creation - Ting Yu; Hailin Liu; Lihua Zhang; Hongbing Liu (Scientific Reports, 2023)
Abstract In recent years, service-oriented computing technology has developed rapidly. The growing number of services increases the choice burden of software developers when developing service-based systems, such as mashups or applications. How to recommend appropriate services for developers to create mashups has become a basic problem in service-oriented recommendation systems. To solve this problem, people have proposed various methods to recommend services to match the requirements of the new mashups and achieved great success. However, there are also some challenges in feature utilization and text requirement understanding. Therefore, we propose a Mashup-oriented Service Recommendation framework based on Deep Learning, called MSRDL. A content component was designed in MSRDL to generate the representation of mashups and services. Besides, an interaction component was created in MSRDL to model the invocation records between mashups and services. The output features of the two parts are further integrated into MLP to obtain the service recommendation lists. Experimental results on ProgrammableWeb datasets show that our method is superior to the state-of-the-art methods.