Multi-label Recommendation of Web Services with the Combination of Deep Neural Networks

With the increasing number of web services on the Internet, how to effectively classify and recommend service labels has become a research issue. It plays an important role in web service organization and management. However, the deficiency of current approaches is that they either recommend only a single label for a web service or a set of independent labels without order ranking that is still difficult for service providers to publish their web services. In this paper, together with label embedding techniques, we propose a novel approach for service multi-label recommendation using deep neural networks. Unlike traditional approaches, the predicted service labels of our approach not only satisfy the demands of service multi-label recommendation, but also provide the importance with an ordered label ranking. The experiments are conducted to validate the effectiveness on a large-scale dataset from ProgrammableWeb, involving 13,869 real-world Web services. The experimental results demonstrate that our approach for multi-label recommendation of web services outperforms the competing approaches in terms of multiple evaluation metrics.