Hybrid Collaborative Filtering with Attention CNN for Web Service Recommendation

Abstract

Service-oriented computing has significantly affect the software development in Web 2.0 era, computing diagram and architectures based on Web services were comprehensively developed. As the Web services were continuously increasing, it becomes more difficult for users to screen out Web services that meet their needs and with good quality while facing with a large amount of Web Services. Therefore, how to recommend the best Web services for users has become a hot research direction in the domain of service computing. Many machine-learning approaches, especially CF (collaborative filtering) models based on matrix factorization, has been widely used in service recommendation tasks. However, it is tough for CF models to deal with sparse invocation matrix when capturing the complicate interaction relation between mashups and services, which would result in a bad performance. To solve this problem, we proposed a hybrid collaborative filtering with attention CNN model for web service recommendation by combining collaborative filtering and attention CNN. The mashup-service invocation matrix as well as attention-based CNN are seamlessly integrated into deep neural nets, which could be used to capture the complicated mashup-service relationships. The experiment result we gain could validate that our proposed models performs better than several state-of-the-art approaches in service recommendation tasks, and further demonstrate the effectiveness of our models.