Service Recommendation Method based on Multi Model Fusion

Abstract

In recent years, the rapid development of service-oriented computing technology has increased the burden of choice for software developers when developing service-based applications. Existing Web service recommendation systems often face two challenges. First, developers are required to input keywords for service search, but due to their lack of knowledge in the relevant field, the keywords entered by the developers are usually freestyle, causing an inability to accurately locate services. Second, it is exceedingly difficult to extract services that meet the requirements due to the 99.8% sparseness of the application service interaction records. To address the above challenges, a framework for service recommendation through multi-model fusion (SRM) is proposed in this paper. Firstly, we employ graph neural network algorithms to deeply mine historical records, extract the features of applications and services, and calculate their preferences. Secondly, we use the BERT model to analyze text information and use the attention mechanism and fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. The two models mentioned above are further merged to obtain the final service recommendation list. Extensive experiments on datasets demonstrate that SRM can significantly enhance the effectiveness of recommendations in service recommendation scenarios.